Copper (Cu) is a critical element for cancer cell proliferation and considerably accumulates in the nucleus. 64Cu2+ is an anticancer radiopharmaceuticals targeting the copper requirement of cancer cells. However, intravenously injected 64Cu2+ ions primarily accumulate in the liver. Ligand complexation of 64Cu2+ would be a promising method for increasing tumor delivery by reducing liver uptake. Herein, we used three tripodal amine ligands [tris(2-aminoethyl)amine (Tren), diethylenetriamine (Dien), and tris(2-pyridylmethyl)amine (TPMA)] to enclose 64Cu2+ ions and compared their in vivo tumor and liver uptake using a tumor-bearing xenograft mouse model of the extrahepatic bile duct carcinoma cell line TFK-1. We examined intracellular Cu distribution using microparticle-induced X-ray emission (micro-PIXE) analysis for these compounds. 64Cu2+-Tren and 64Cu2+-Dien showed higher tumor uptake than 64Cu2+-TPMA and 64Cu2+ ions in TFK-1 tumors. Among the three 64Cu2+ complexes and 64Cu2+ ions, liver uptake was inversely correlated with tumor uptake. Micro-PIXE analysis showed that in vitro cellular uptake was similar to in vivo tumor uptake and nuclear delivery was the highest for 64Cu2+-Tren. Conclusively, an inverse correlation between tumor and liver uptake was observed using three 64Cu2+ complexes of tripodal amine ligands and 64Cu2+ ions. These results provide useful information for the future development of anticancer 64Cu radiopharmaceuticals.

The achievement of size uniformity and monodispersity in perovskite quantum dots (QDs) requires the implementation of precise temperature control and the establishment of optimal reaction conditions. Nevertheless, the accurate control of a range of reaction variables represents a considerable challenge. This study addresses the aforementioned challenge by employing manganese (Mn) doping to achieve size uniformity in CsPbBr₃ perovskite QDs without the necessity for precise control of the reaction conditions. By optimizing the Mn:Pb ratio, it is possible to successfully dope CsPbBr₃ QDs with appropriate concentrations of Mn²⁺ and achieve a uniform size distribution. The spectroscopic measurements on single QDs indicate that the appropriate Mn²⁺ concentrations can result in a narrower spectral linewidth, a longer photoluminescence (PL) lifetime, and a reduced biexciton Auger recombination rate, thus positively affecting the PL properties. This study not only simplifies the size control of perovskite QDs, but also demonstrates the potential of Mn-doped CsPbBr₃ QDs for narrow linewidth light-emitting diode applications.

The electric vehicle (EV) industry challenges battery joining technologies by requiring higher energy density both by mass and volume. Improving the energy density via new battery chemistry would be the holy grail but is seriously hindered and progresses slowly. In the meantime, alternative ways, such as implementing more efficient cell packaging by minimizing the electrical resistance of joints are of primary focus. In this paper we discuss the challenges associated with the electrical characterisation of laser soldered joints in general, and the minimization of their resistive losses, in particular. In order to assess the impact of the joint resistance on the overall resistance of the sample, the alteration in resistance was monitored as a function of voltage probe distance and modelled by finite element simulation. The experimental measurements showed two different regimes: one far from of the joint area and another in its vicinity and within the joints cross section. The presented results confirm the importance of the thickness of the filler material, the effective and the total soldered area, and the area and the position of the voids within the total soldered area in determining the electrical resistance of joints.

A challenge in building large-scale superconducting quantum processors is the precise control and manipulation of the qubit state. However, the crosstalk between the microwave control lines impedes the parallel execution of high-fidelity digital and analog quantum operations. Here, we propose and demonstrate a universal compensation protocol for calibrating the microwave signal crosstalk. We also introduce amplified error sequences to optimize the accuracy. Furthermore, we show a definitive improvement in parallel gate operations with crosstalk cancellation, demonstrating the technique's effectiveness. This work paves the way for superconducting hardware that features automated calibration of microwave crosstalk, leading to enhanced fidelities in multiqubit circuits.

We propose and experimentally realize a scheme of the optical nonreciprocity (ONR) using degenerate two-level atoms embedded in an optical ring cavity. For the degenerate transition Fg=4→Fe=3, we first study the cavity-transmission property in different coupling field, and verify that within the strong-coupling regime, the single-dark-state peak formed by electromagnetically induced transparency (EIT) shows ONR. Qualitative theoretical analysis shows that the stable ground-state Zeeman coherence is necessary for intracavity EIT. However, due to the frequency degeneracy, the large Rabi frequency of counter-propagating coupling field can reduce the number of effective atoms and weaken the strong-coupling of the atom-cavity, which determines that ONR based on intracavity EIT occurs only at weaker probe intensity. Furthermore, the ONR transmission with high contrast, linewidth-narrowed and broadband is experimentally demonstrated.

We communicate the theoretical results in the study of utilizing the quantum phenomena in optical current for quantum computation in the context of high precision voltage measurements. These results can be used around computation by quantum sampling and quantum communication as the basis for further research and physical implementations. We propose a Main Optical Setup (MOS) for such computation which allows to make a superstructure to implement specific computations and algorithms. To create MOS we used the nonlinear units (e.g., beta- barium borate crystal), arranged in series, powered with pulsed laser pump, and ended with the beam splitter to generate the output state of a number of entangled photon pairs. The computation is made by propagation of entanglement with beam splitters applied crossword and adjustable phase shifters that are tools for parameter steering. We show how to implement the series of cosine-based components on the example of two-component case. The results opens a broad area for future research in the area of building quantum optimizer using the quantum sampling methods and in the area of high precision temporal measurement of voltage, which is the important process for building high-fidelity devices.

Quantum cryptography revolutionizes secure information transfer, providing defense against both quantum and classical computational attacks. The primary challenge in extending the reach of quantum communication comes from the exponential decay of signals over long distances. Addressing this challenge, we have developed Quantum-protected Control-based Key Distribution (QCKD), overcoming transmission range limitations by monitoring signal leakages and rendering leaked quantum states significantly non-orthogonal. This paper reports on the experiments with the QCKD, conducted over a 1,707 km fiber line. We demonstrate advancements in critical components of the protocol, showing its remarkable scalability and adaptability for extended distances and multi-user configurations.

The authors previously found a model of universal quantum computation by making use of the coset structure of subgroups of a free group $G$ with relations. A valid subgroup $H$ of index $d$ in $G$ leads to a \lq magic' state $\left|\psi\right\rangle$ in $d$-dimensional Hilbert space that encodes a minimal informationally complete quantum measurement (or MIC), possibly carrying a finite \lq contextual' geometry. In the present work, we choose $G$ as the fundamental group $\pi_1(V)$ of an exotic $4$-manifold $V$, more precisely a \lq small exotic' (space-time) $R^4$ (that is homeomorphic and isometric, but not diffeomorphic to the Euclidean $\mathbb{R}^4$). Our selected example, due to to S. Akbulut and R.~E. Gompf, has two remarkable properties: (a) it shows the occurence of standard contextual geometries such as the Fano plane (at index $7$), Mermin's pentagram (at index $10$), the two-qubit commutation picture $GQ(2,2)$ (at index $15$) as well as the combinatorial Grassmannian Gr$(2,8)$ (at index $28$) , (b) it allows the interpretation of MICs measurements as arising from such exotic (space-time) $R^4$'s. Our new picture relating a topological quantum computing and exotic space-time is also intended to become an approach of \lq quantum gravity'.

The Aether Physics Model (APM) presents a radical reinterpretation of fundamental physics, offering potential solutions to long-standing problems in Quantum Mechanics and General Relativity. This paper explores the core concepts and equations of the APM, demonstrating how they provide a unified framework for understanding the nature of space, time, matter, and fundamental forces. We examine the implications of the APM's quantized Aether structure, the role of the Singularity and Gforce, and the reinterpretation of fundamental constants and forces. The model's predictions regarding dark matter, neutron content in stable matter, and the unification of forces are discussed, along with potential experimental tests. Additionally, we introduce the loxodrome concept and toroidal particle topology as central elements in explaining fundamental particles and forces. While highly speculative, the APM offers a thought-provoking alternative to standard physics models and warrants further investigation.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

Warren Weaver, writing about the function that science should have in mankind’s developing future, ideas and ideals, proposed to classify scientific problems into ‘problems of simplicity’, ‘problems of disorganised complexity’, and ‘problems of organised complexity’ — the huge complementary class to which all biological, human, and social problems belong. Problems of simplicity have few components and variables and have been extensively addressed in the last 400 years. Problems of disorganised complexity have a huge number of individually erratic components and variables, but possess collective regularities that can be analysed by resourcing to stochastic methods. Yet, problems of organised complexity do not yield easily to classical or statistical treatment since interrelations among phenomenon elements change during its evolution alongside commonly used state variables, affecting behaviour and outcome. Moreover, organisation, the focal point in this complementary class, is still an elusive concept despite the gigantic efforts undertaken since a century ago to tame it. This paper addresses the description, representation and study of phenomena in the ‘problems of organised complexity’ class. Grounded on relational mathematical constructs, a theoretical framework providing operational definitions and schemes for formally modelling organisations and interaction of organisations is introduced. This framework extends the general systems’ concept and provides a novel perspective for addressing organised complexity phenomena as a collection of interacting organisations.

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

We study Einstein's gravity coupled to nonlinear electrodynamics with two parameters in Anti-de Sitter spacetime. Magnetically charged black holes in an extended phase space is investigated. We obtain the mass and metric functions, their asymptotic and corrections to the Reissner--Nordstr\"{o}m metric function when the cosmological constant vanishes. The first law of black hole thermodynamics in extended phase space is formulated and the magnetic potential and the thermodynamic conjugate to the coupling are obtained. We proved the generalized Smarr relation. The heat capacity and the Gibbs free energy are computed and phase transitions are studied. It was shown that the electric field of charged objects at the origin and electrostatic self-energy are finite within the nonlinear electrodynamics proposed.

This paper's aim is to extend horizons in symbolic AI. The key to doing so is to establish a scientific basis for knowledge foundations, and specifically for the concept of meaning, and its computation. This led to the creation of the Meaning Computation AI (MCAI). Knowledge structures are at the heart of MCAI. The semantic triple chain, one of such structures, is the subject of this paper. This structure is the core of introducing computational semantic reasoning models across a variety of domains. To efficiently apply knowledge structures to create semantic reasoning applications MCAI contains a development framework consisting of four components. The first component stems from our discovery that knowledge is relational in nature. It can be expressed through implicit relations (which reflect meaning) and semantic relations (which describe how implicit relations act between entities). An algebraical apparatus for relational knowledge, specifically for implicit and semantic relations, represents the first component. Axiomatic models of relational domain ontologies and domain-oriented semantic reasoning models comprise the second and third components. The fourth component contains domain-oriented semantic reasoning engines.

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for building, implementing, and simulating quantum hardware.

In this paper, we introduce a novel controlled quantum communication protocol utilizing a quantum random walk involving one sender, one receiver, and multiple controllers. Inspired by classical random walk theory, quantum random walk serves as the foundation of our proposed protocol. With this protocol, we demonstrate the ability to transfer any n- dimensional quantum state to any party facilitated by any m number of controllers. Furthermore, any of the (m+1) individuals has the freedom to accept the role of the receiver. Rigorous testing of the protocol’s performance is conducted through quantum state tomography. We have run different variations of the circuits in IBMQ QASM simulator. Additionally, we explore the effectiveness of a weak measurement-based protocol designed to mitigate amplitude-damping noise’s detrimental effects on quantum states. By analyzing fidelity versus amplitude damping noise-strength plots for scenarios with and without the weak measurement protocol, we provide valuable insights into its protective capabilities across various levels of noise. These find- ings illuminate the protocol’s potential applications in quantum information processing

This paper discusses about the quantum energy levels of muonic hydrogen considering only Coulomb interaction. The computational analysis in this paper will help the reader get a good understanding of how different a muonic hydrogen is from its electronic counterparts. Energy eigenvalues are calculated by numerical method and the probability density distribution of muon around the nucleus is plotted for different quantum states using special functions. The code is written in python. A comparative study of emission spectra between the muonic hydrogen and hydrogen has also been discussed.

In this paper, the feasibility of automated and accurate in vivo measurements of vascular parameters continuously and non-invasively using ultrasound sensor is presented. Vascular parameters such as pulse wave velocity (PWV), blood pressure (BP), arterial compliance (AC) and stiffness index (SI) are affluent indicators of cardiovascular disorders and needs to be monitored non-invasively and continuously during surgeries and follow-up procedures. Cuff based or invasive catheter techniques are considered as gold standard to measure BP and are fed manually to compute AC and SI which employ imaging algorithms. In this context, a Continuous and Non-Invasive Vascular Stiffness and Arterial Compliance Screener (CaNVAS) is developed to measure said parameters continuously and non-invasively using ultrasound sensor. Acoustic waves of 5 MHz (2.2 – 10 MHz) are driven through target arterial walls, reflected echoes captured, pre-processed and frequency shift is used to calculate PWV. It is observed that PWV measured using CaNVAS varies exponentially with BP values obtained from sphygmomanometer (BPMR-120) and this relationship is used to compute instantaneous values of BP. The proposed device is validated by performing measurements on 250 subjects in pre and post exercise conditions and found to have 95% accuracy and an average of 12.5% coefficient of variation.

The problem of reliable function computation is extended by imposing privacy, secrecy, and storage constraints on a remote source whose noisy measurements are observed by multiple parties. The main additions to the classic function computation problem include 1) privacy leakage to an eavesdropper is measured with respect to the remote source rather than the transmitting terminals’ observed sequences; 2) the information leakage to a fusion center with respect to the remote source is considered as a new privacy leakage metric; 3) the function computed is allowed to be a distorted version of the target function, which allows to reduce the storage rate as compared to a reliable function computation scenario in addition to reducing secrecy and privacy leakages; 4) two transmitting node observations are used to compute a function. Inner and outer bounds on the rate regions are derived for lossless and lossy single-function computation with two transmitting nodes, which recover previous results in the literature. For special cases that include invertible and partially-invertible functions, and degraded measurement channels, exact lossless and lossy rate regions are characterized, and one exact region is evaluated for an example scenario.

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than fully-connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multi-features in multidimensional data. In this paper, we present a multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multi-feature quantum convolution with average and Euclidean pooling and thus adapting the CNN structure to a variational quantum algorithm (VQA). Average pooling is based on the quantum Haar transform (QHT) and Euclidean pooling is based on partial quantum measurement. The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results showed favorable characteristics of our proposed techniques compared to existing work with respect to a number of quantitative metrics such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

In this study, based on a new quantum integral identity, the authors establish some quantum estimates of Hermite-Hadamard type inequalities for convex functions. These results generalize and improve some known results given in literatures.

We study the electron tunneling (ET) and local Andreev reflection (AR) processes in a quantum dot (QD) coupled to the left and right ferromagnetic leads with noncollinear ferromagnetisms. In particular, we consider that the QD is also side-coupled to a nanowire hosting Majorana bound states (MBSs) at its ends. Our results show that when one mode of the MBSs is coupled simultaneously to both spin-up and spin-down electrons on the QD, the height of the central peak is quite different from that if the MBS is coupled to only one spin component electrons. The ET and AR conductances which are mediated by the dot-MBS hybridization strongly depend on the angle between the left and right magnetic moments in the leads, which result in sign change of the angle-dependent tunnel magnetic resistance. This result is very different from case when the QD is coupled to regular fermonic mode, and can be used for detecting the existence of MBSs, a current challenge in condensed matter physics under extensive investigations.

The assessment of Histone deacetylases in conjunction with Curcumin, conducted through the Mcule Database employing the Autodock Vina algorithm, involves an examination of molecular interactions. This analysis includes the calculation of binding energies, revealing the strength and stability of the interactions between Curcumin and various Histone deacetylase targets. The comparison encompasses diverse aspects such as binding energies, specific binding sites, interactions across different targets, structural details, and potential implications for biological activity. The results imply that Curcumin shows heightened affinity for Histone deacetylase 4 in comparison to other investigated histone deacetylases.

An increasing amount of electric energy is consumed by computers as they progress in function and capabilities. All of it is dissipated in heat during the computing and communicating operations and we reached the point that further developments are hindered by the unbearable amount of heat produced. In this paper we briefly review the fundamental limits in energy dissipation, as imposed by the laws of physics, with specific reference to computing and memory storage activities.

Thermal camouflage is a highly sought-after technology to increase the survivability of military equipment against infrared (IR) detectors. Recently, two-dimensional (2D) nanomaterials have shown low IR emissivity, widely tunable opto-electronic properties and compatibility with stealth-applications. Among them, graphene and graphene-like materials are the most attractive 2D materials used for thermal camouflage applications. In particular, in multilayer graphene (MLG) charge density can be effectively tuned through sufficiently intense electric fields or through electrolytic gating. Therefore, MLG optical properties, like infrared emissivity and absorbance, can be controlled in a wide range by voltage bias. The large emissivity modulation achievable with this material makes it suitable in the design of thermal dynamic camouflage devices. Generally, the emissivity modulation in the multilayered graphene medium is governed by an intercalation process of non-volatile ionic liquids under a voltage bias. The electrically-driven reduction of emissivity leads to a decrease in the apparent temperature of the surface, which can match that of the background enabling thermal camouflaging. Since this property is common to other graphene-based materials, here we present a review specifically focused on the recent advances in the field of the thermal camouflage properties of graphene in the form of composite film and aerogel structures. A summary of the current understanding of the working principle of thermal camouflage materials, current limitations, and future opportunities is presented and discussed.

We prove that the Legendre coefficients associated with a function f(x) can be represented as the Fourier coefficients of a suitable Abel-type transform of the function itself. Thus, the computation of N Legendre coefficients can be performed efficiently in O(NlogN) operations by means of a single Fast Fourier Transform of the Abel-type transform of f(x). We also show how the symmetries associated with the Abel-type transform can be exploited to further reduce the computational complexity. The dual problem of calculating the sum of Legendre expansions is also considered. We prove that a Legendre series can be written as the Abel transform of a suitable Fourier series. This fact allows us to state an efficient algorithm for the evaluation of Legendre expansions. Finally, numerical tests are presented to exemplify and confirm the theoretical results.

We introduce optically clear and resilient free-form micro-optical of pure (non-photosensitized) organic-inorganic SZ2080 material made by femtosecond 3D laser lithography (3DLL). This is advantageous for rapid printing of 3D micro-/nanooptics, including their integration directly onto optical fibers. A systematic study on the fabrication peculiarities and quality of resultant structures is performed. Comparison of microlenses’ resiliency to CW and femtosecond pulsed exposure is determined. Experimental results prove that pure SZ2080 is ∼3 fold more resistant to high irradiance as compared with a standard photo-sensitized material and can sustain up to 1.91 GW/cm² intensity. 3DLL is a promising manufacturing approach for high-intensity micro-optics for emerging fields in astro-photonics and atto-second pulse generation. Additionally, pyrolysis is employed to shrink structures up to 40% by removing organic SZ2080 constituents. This opens a promising route towards downscaling photonic lattices and creation of mechanically robust glass-ceramic structures.

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal-oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing QCA irreversible, reversible, and partially reversible gates to establish an optimal balance between power consumption, delay time, and occupied area. The hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We have evaluated the hybrid design method by examining the half-adder circuit as a case study. We have developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level, within a microscopic quantum mechanical model.

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let’s delve into the mathematical intricacies of these sequences. We’ve previously devised a mathematical approach for defining a “healthy” sequence (10.14218/GE.2023.00079). This sequence is characterized by having at most four distinct nucleotides (denoted as nt≤4). It serves as the generator of a group denoted as fp. The desired properties of this sequence are as follows: fp should be close to a free group of rank nt−1, it must be aperiodic, fp should not have isolated singularities within its SL2(C) character variety (specifically within the corresponding Groebner basis). Now, let’s explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as S24. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI equation (10.3390/dynamics4010001). In certain cases, S24 degenerates, in the sense that two punctures collapse, resulting in a “wild” dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find applications in cancer research.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

Thrombosis is the pathological clot formation under abnormal hemodynamic conditions, which can result in vascular obstruction, causing ischemic strokes and myocardial infarction. Thrombus growth under moderate to low shear (<1000 s^-1) relies on platelet activation and coagulation. Thrombosis at elevated high shear rates (>10,000 s-1) is predominantly driven by unactivated platelet binding and aggregating mediated by von Willebrand factor (VWF), while platelet activation and coagulation are secondary in supporting and reinforcing the thrombus. Given the molecular and cellular level information it can access, multiscale computational modelling informed by biology can provide new pathphysiological mechanisms that are otherwise not accessible experimentally, holding promise for novel first-principle-based therapeutics. In this review, we summarize the key aspects of platelet biorheology and mechanobiology, focusing on the molecular and cellular scale events and how they build up to thrombosis through platelet adhesion and aggregation in the presence or absence of platelet activation. In particular, we highlight recent advancements in multiscale modeling of platelet biorheology and mechanobiology and how they can lead to better prediction and quantification of thrombus formation, exemplifying the exciting paradigm of digital medicine.

In the current era of emerging technologies and cutting-edge inventions, it is equally important to consider the ethical aspects of research and innovations to have an ethical space for the sustainable progress and safety of humanity. The field of nanotechnology research and innovation often faces criticism due to its potential adverse impacts on health, the environment, limited accessibility, high costs, and concerns about data privacy. Therefore, it is crucial to address and resolve these ethical issues systematically. In this regard, we present our findings and methods for exploring the application of the ethics technology assessment method by considering its relevance at different stages of innovation development. Specifically, the present work provides a conceptual framework for ethics technology assessment focused on evaluating a significant innovation in nanotechnology within the domain of food safety with specific reference to mycotoxin remediation. By leveraging the value-sensitive design approach, we described various strategies for analyzing the ethical implications of nanotechnology-based innovations in mycotoxin remediation. Through comprehensive ethics technology assessment, nanotechnology-based innovations can be guided toward becoming more value-oriented, thereby making substantial contributions to social progress.

The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest - pain generates intense feelings and absorbs all our conscious awareness yet nothing particularly intelligent is going on. Still, in the hard sciences, this topic is frequently met with skepticism because to date no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human experience through brain-quantum computer interfaces.

Quantum nonlocality represents correlation properties between subsystems of a composite quantum system, usually including the four types: Bell nonlocality, steerability, entanglement, and quantum correlation (quantum discord). Given a basis $e_{AB}=\{|e_{ij}\>\}_{i\in[d_A],j\in[d_B]}$ for the Hilbert space $\H_A\otimes \H_B$ of a bipartite system $AB$, a density operator $\rho^{AB}$ (quantum state) of $AB$ can be represented as a $d_Ad_B\times d_Ad_B$ matrix $\hat{\rho}_{e_{AB}}=[\]$, called the density matrix of a density operator $\rho^{AB}$. A natural question is what is the relationship between the quantum nonlocality of a density operator $\rho^{AB}$ and its corresponding density matrix $\hat{\rho}_{e_{AB}}$? In this work, we discuss the relationships between quantum locality and basis, and prove that one type of quantum locality of density operators and that of their density matrices under a basis are the same if and only if the chosen basis is the tensor product of the bases of subsystems. Consequently, different bases define different quantum nonlocality density operators.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

This paper draws upon the theory of turbulence in physics to explore the similarities and differences between volatility in financial markets and turbulent phenomena at a statistical physics level. By analogizing the dynamics of financial markets with fluid turbulence, we have developed an innovative analytical framework aimed at deepening the understanding of the complexity inherent in financial markets. Our research methodology includes a comparative analysis of simulated time series data for the Standard & Poor's 500 Index and fully developed turbulent velocity time series. We focus on the similarities in key statistical physical properties, including probability distributions, correlation structures, and power spectral densities. Furthermore, we have established a model of capital flow in financial markets and proposed corresponding solutions. Through computational simulations and data analysis, we find that while financial market volatility shares some statistical characteristics with turbulent phenomena, significant differences exist in the shapes of probability distributions and the temporal scales of correlations. This suggests that although financial markets exhibit patterns akin to turbulence, the behavior of this complex, multi-variable driven system does not completely correspond to natural turbulence, highlighting the limitations of directly applying turbulence theory to financial market analysis. Our study offers a new perspective for the theories of financial markets and the field of econophysics, providing fresh insights into the complexity of financial markets and contributing to the prevention and management of financial risk.

From single-cell organisms to complex neural networks, all evolved to provide control solutions to generate context and goal-specific actions. Neural circuits performing sensorimotor computation to drive navigation employ inhibitory control as a gating mechanism, as they hierarchically transform (multi)sensory information into motor actions. Here, we focus on this literature to critically discuss the proposition that prominent inhibitory projections form sensorimotor circuits. After reviewing the neural circuits of navigation across various invertebrate species, we argue that with increased neural circuit complexity and the emergence of parallel computations inhibitory circuits acquire new functions. The contribution of inhibitory neurotransmission for navigation goes beyond shaping the communication that drives motor neurons, instead, include encoding of emergent sensorimotor representations. A mechanistic understanding of the neural circuits performing sensorimotor computations in invertebrates will unravel the minimum circuit requirements driving adaptive navigation.

The k-means is one of the most popular and widely used clustering algorithm, however, it is limited to only numeric data. The k-prototypes algorithm is one of the famous algorithms for dealing with both numeric and categorical data. However, there have been no studies to accelerate k-prototypes algorithm. In this paper, we propose a new fast k-prototypes algorithm that gives the same answer as original k-prototypes. The proposed algorithm avoids distance computations using partial distance computation. Our k-prototypes algorithm finds minimum distance without distance computations of all attributes between an object and a cluster center, which allows it to reduce time complexity. A partial distance computation uses a fact that a value of the maximum difference between two categorical attributes is 1 during distance computations. If data objects have m categorical attributes, maximum difference of categorical attributes between an object and a cluster center is m. Our algorithm first computes distance with only numeric attributes. If a difference of the minimum distance and the second smallest with numeric attributes is higher than m, we can find minimum distance between an object and a cluster center without distance computations of categorical attributes. The experimental shows proposed k-prototypes algorithm improves computational performance than original k-prototypes algorithm in our dataset.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

The paper describes a new formula for calculating the mutual inductance between a thin conical sheet inductor and a filamentary circular loop, which are coaxial. The presented formula is derived semi-analytically using the complete elliptic integrals of the first, second, and the third kind, along with the integral term which will be solved numerically. The results are validated using double and single integration methods, as well as the semi-analytical formula. The mutual inductance between a thin wall solenoid and a filamentary circular loop can be obtained using the new formula for the conical coil and circular loop. Presented formulas can be useful in various applications, such as broadband RF and wireless power transfer systems that utilize conical inductors. Overall, the paper presents a valuable contribution to the field of inductor design and can be useful in various applications involving conical inductors.

One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.

A parallel shallow water flow model is introduced in this paper. The explicit-time finite volume approach is adopted to solve the 2D shallow water equations on an unstructured triangular mesh. The proposed scheme is second-order accurate in temporal and spatial terms using the two-stage Runge-Kutta and the monotone upwind scheme for conservation law (MUSCL) methods, respectively. Based on Message Passing Interface (MPI) and OpenACC, a multi-GPU model is presented with the METIS library to produce the domain decomposition. A CUDA-aware MPI library through GPUDirect for peer-to-peer (P2P) transfer between two GPUs and overlapping computation and MPI communication are used to speed up MPI memory exchange and the performance of the code. A 2D circular dam break test with wet and dry downstream beds and grid resolutions of about 2 million cells is considered to verify the accuracy of the code, and good results were achieved compared to the numerical simulations of published studies. Compared with the multi-CPU version of the 6-core CPU, maximum speedups of 56.18 and 331.51 were obtained using the single-GPU and multi-GPU versions, respectively. Results indicate that acceleration performance improves as the mesh resolution increases.

Most neuromorphic technologies use a point-neuron model, missing the spatiotemporal nature of neuronal computation performed in dendrites. Dendritic morphology and synaptic organization are structurally tailored for spatiotemporal information processing, enabling various computations like visual perception. Here, we report on a neuromorphic computational model termed ‘dendristor’, which integrates functional synaptic organization with dendritic tree-like morphology computation. The dendristor presents bioplausible nonlinear integration of excitatory and inhibitory synaptic inputs with silent synapses and diverse spatial distribution dependency. We show that the dendristor can emulate direction selectivity, which is the feature to react robustly to a preferred signal direction on the dendrite. We discover that silent synapses can remarkably enhance direction selectivity, turning out to be a crucial player in dendritic computation processing. Finally, we develop neuromorphic dendritic neural circuits that can emulate a cognitive function such as motion perception in the retina. Using dendritic morphology, we achieve visual perception of motion in 3D space by various mapping of spatial information on different dendritic branches. This neuromorphic dendritic computation innovates beyond current neuromorphic computation and provides solutions to explore new skylines in artificial intelligence, neurocomputation, and brain-inspired computing.

Drug design is used for different applications of bioinformatics tools analyze DNA, genome, and sequence target region of a small organic molecule in order to understand the molecules of disease. Bioinformatics tools are identified a newly wide research field and minimize future risks through web servers and data mining. Clinical sample test performed with the bioinformatics tools as the biomedical detective. A particular structure and configuration of protein obliging in Drug design concluded Bioinformatics. This review bioinformatics tools and webserver will discuss functions of small organic molecules according to clinical pharmacology.

We study theoretically the Josephson diode effect (JDE) realized in a system composed of parallel-coupled double quantum dots (DQDs) sandwiched between two semiconductor nanowires deposited on an s-wave superconductor surface. Due to the combined effects of the proximity-caused superconductivity, strong Rashba spin-orbit interaction and Zeeman splitting inside the nanowires, a pair of Majorana bound states (MBSs) may possibly emerge at opposite ends of each nanowire. Different phase factors arisen from the superconductor substrate can be generated in the coupling amplitudes between the DQDs and MBSs prepared at the left and right nanowires, and will result in the Josephson current. We find that the critical Josephson currents in positive and negative directions are different from each other in amplitude within an oscillation period with respective to the magnetic flux penetrating through the system, a phenomenon known as the JDE. It arises from the quantum interference effect in this double-path device, and can hardly occur in system of one QD coupled to MBSs. Our results also show that the diode efficiency can reach up to 50%, and depends on the overlap amplitude between the MBSs and the energy levels of the DQDs adjustable by gate voltages. The present model is realizable within current nanofabitication technologies and may find practical use in interdisciplinary field of Majorana and Josephson physics.

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

Investigations into the properties of generalized effective temperature are conducted across arbitrary dimensions. Maxwell-Boltzmann distribution is displayed for one, two, and three dimensions, with effective temperatures expressed for each dimension. The energy density of blackbody radiation is examined as a function of dimensionality. Effective temperatures for non-uniform temperature distributions in one, two, three, and higher dimensions are presented, with generalizations extended to arbitrary dimensions. Furthermore, the application of generalized effective temperature is explored not only for linearly non-uniform temperature distributions but also for scenarios involving the volume fraction of two distinct temperature distributions. Finally, the effective temperature is determined for a cryogenic system supplied with both liquid nitrogen and liquid helium.

The Mann-Whitney effect is a measure for comparing survival distributions between two groups. The Mann-Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann-Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent each other, the traditional expression of the Mann-Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann-Witney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands (https://nkosuke.shinyapps.io/shiny_survival/). Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

This study explores the properties and behavior of bi-close-to-convex functions, introducing new subclasses defined by their relationship with fractional differential operator and bi-univalent functions. Using the Faber polynomial technique, we derive upper bounds for the n^{th} coefficient of functions in these classes. We also investigate the erratic behavior of initial coefficients in bi-close-to-convex functions, as characterized by the (λ,q)-fractional differintegral operator. Furthermore, we address Fekete-Szegö problems and present notable findings from our investigation, contributing to the continued growth and refinement of geometric function theory, yielding new insights and practical uses.

1) We shall discuss what modern interpretations say about the Heisenberg's uncertainties. These interpretations explain that a quantity begins to 'lose' meaning when a conjugate property begins to 'acquire' definite meaning. We know that a quantity losing meaning means that it has no fixed value and has an uncertainty . In this paper we look deeper into this interpretation and the outcome reveals more evidence of the quantity losing meaning. Newer insights appear. 2) We consider two extreme cases of hypothetical processes nature undergoes, without interference by a measurement: One, a system collapses to an energy eigenstate under the influence of a Hamiltonian instantaneously at a time $t$. This is thus what would happen if we would measure the system's energy. Next, when a particle becomes localised to a point at a time $t_0$ under the influence of a Hamiltonian. This is thus what would happen if we would measure the system's position. We shall prove that both these situations cannot arise under ordinary circumstances and thus measurement processes cannot be modelled by physical Hamiltonians.

In this work the existence and nonexistence of stationary radial solutions to the elliptic partial differential equation arising in the molecular beam epitaxy are studied. Since we are interested in radial solutions we arrive at the following fourth-order differential equation

We are concerned with the use of some classical spectral collocation methods as well as with the new software system Chebfun in order to compute high order (index) eigenpairs of singular as well as regular Schrodinger eigenproblems. We want to highlight both the qualities as well as the shortcomings of these methods and evaluate them vis-a-vis the usual ones. In order to resolve a boundary singularity we use Chebfun with the simple domain truncation technique. Although this method is equally easy to apply with spectral collocation, things are more nuanced in the case of these methods. A special technique to introduce boundary conditions as well as a coordinate transform which maps an unbounded domain to a nite one are the ingredients. A challenging set of "hard" benchmark problems, for which usual numerical methods (f. d., f. e. m., shooting etc.) fail, are analysed. In order to separate "good"and "bad"eigenvalues we estimate the drift of the set of eigenvalues of interest with respect to the order of approximation and/or scaling of domain parameter. It automatically provides us with a measure of the error within which the eigenvalues are computed and a hint on numerical stability. We pay a particular attention to problems with almost multiple eigenvalues as well as for problems with a mixed (continuous) spectrum. In the latter case we try to numerically highlight its existence. Special attention will be paid to the higher eigenpairs (the pair of eigenvalue and the corresponding eigenfunction approximated by an eigenvector spanning its nodal values).

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

The performance analysis of the internal/external flows around a high speed vehicle in near-space is fundamental and critical to the configuration and structure design for high speed aircraft. In this paper, the two-dimensional coupled implicit Reynolds Average Navier-Stokes (RANS) solver and RNG k–ε turbulence model is employed to numerically simulate the flow field of an integrated high speed vehicle. The flow field are divided three parts to solve separately and compared with that solved by integrated simulation. The flow field are decomposed to save computer storage and simulation time. The first part is inlet/isolator, the second part is combustion chamber and the third is nozzle. In the computation process, the outlet condition of isolator is endowed to the inlet boundary of combustion chamber and the outlet boundary of combustion chamber is endowed to the inlet boundary of nozzle, then the whole flow field of integrated high speed vehicle is solved. Aerodynamic characteristic of fragment-computation and integration-computation is comparatively analyzed at different angles of attack, the results show that the maximum deviation of lift coefficient obtained by fragment-computation is less than 3% compared with that by integration, the maximum deviation of drag coefficient fragment-computation is less than 5% compared with that by integration, while pitching moment coefficient deviation less than 12%. However, for simulation time-consumption, the time used by fragment-computation reduces 45% compared with integration-computation. Meanwhile, by components force analysis, it can be concluded that the aerodynamic property of inlet/isolator can been obtained separately and hardly effected by other parts.

The two-scale method (TSM) is an effective approach to simulate electromagnetic (EM) scattering from rough sea surface, to interpret physical interaction of EM wave with rough sea surfaces. The sea surface roughness spectrum and the cutoff wavenumber are two key factors affecting the accuracy of TSM simulation. This paper takes the geophysical model functions (GMFs) of different band as reference, and quantitatively studies the two factors based on the numerical simulation of different polarization backscattering coefficients. The numerical simulation demonstrates that Apel spectrum is more suitable for the backscattering simulation in C/X/Ku-band, and the optimal cutoff wavenumbers are also derived for TSM simulation of EM scattering in C/X/Ku-band. Then an empirical model function of optimal cutoff wavenumbers with respect to frequency, incident angle and wind speed are obtained with regression method. Finally, the simulation results with the new model function of cutoff wavenumber are compared with the GMF and measured data in different bands and they are all consistent. Moreover, a physical explanation for the optimal cutoff wavenumber is also given.

This paper discusses the Quantum Temporal Resonator (QTR), a theoretical device leveraging quantum entanglement and metamaterials with negative refraction indices to achieve controlled temporal displacement. By establishing a network of entangled particles within a Bose-Einstein Condensate (BEC) and utilizing a resonant cavity made of advanced metamaterials, the QTR creates localized distortions in space-time. The frequency and amplitude of temporal harmonics within this cavity are finely tuned to allow for precise temporal displacement of matter and information. Numerical simulations demonstrate the coupling between the quantum wave function and electromagnetic fields, validating the theoretical model. The results show distinctive patterns in the wave function and perturbations in the electric field, supporting the feasibility of achieving controlled temporal displacement. This study explores the theoretical framework, mathematical modeling, experimental setup, and potential applications of the QTR, providing a comprehensive analysis of its feasibility and implications.

We discuss the fate of the correspondence principle beyond quantum mechanics, specifically in quantum field theory and quantum gravity, in connection with the intrinsic limitations of the human ability to observe the external world. We conclude that the best correspondence principle is made of unitarity, locality, proper renormalizability (a refinement of strict renormalizability), combined with fundamental local symmetries and the requirement of having a finite number of fields. Quantum gravity is identified in an essentially unique way. The gauge interactions are uniquely identified in form. Instead, the matter sector remains basically unrestricted. The major prediction is the violation of causality at small distances.

The use of convolutional neural networks (CNNs) in image classification has become the standard method of approaching computer vision problems. Here we apply pre-trained networks to classify images of non-breaking, plunging and spilling breaking waves. The CNNs are used as basic feature extractors and a classifier is then trained on top of these networks. The dynamic nature of breaking waves is exploited by using image sequences to gain extra information and improve the classification results. We also see improved classification performance in using pre-computed image features such as the optical flow between image pairs. The inclusion of the dynamic information improves the classification between breaking wave classes. We also provide corrections to the methodology from the article from which the data originates to achieve a more accurate assessment of performance.

A critical discussion on the computation of the dispersion diagram in periodic one-dimensional guiding structures is carried out. In particular, an analysis is presented of the pros and cons of combined methods that make use of full-wave simulations done with commercial software packages with further analytical post-processing based on simplifications brought by an equivalent circuit model of the structure. Some of the most common methods reported in the literature are reviewed and their advantanges and limitations highlighted. Our discussion is complemented with several selected numerical examples in order to discuss the most relevant aspects that a potential user of these methods should be aware of. Special attention is paid to the relevant role played by the high-order coupling between the two halves of a symmetric unit cell of the periodic structure.

In this paper we will show that standard physics to a large degree consists of derivatives of a deeper reality. This means standard physics is both overly complex and also incomplete. Modern physics has typically started from working with first understanding the surface of the world, that is typically the macroscopic world, and then forming theories about the atomic and subatomic world. And we did not have much of a choice, as the subatomic world is very hard to observe directly, if not impossible to observe directly at the deepest level. Despite the enormous success of modern physics, it is therefore no big surprise that we at some point have possibly taken a step in the wrong direction. We will claim that one such step came when one thought that the de Broglie wavelength represented a real matter wavelength. We will claim that the Compton wavelength is the real matter wavelength. Based on such a view we will see that many equations in modern physics are only derivatives of much simpler relations. Second, we will claim that in today’s physics one uses two different mass definitions, one mass definition that is complete or at least more complete, embedded in gravity equations without being aware of it, as it is concealed in GM, and the standard, but incomplete, kg mass definition in non-gravitational physics. First, when this is understood, and one uses the more complete mass definition that is embedded in gravity physics, not only in gravity physics, but in all of physics, then one has a chance to unify gravity and quantum mechanics. Our new theory shows that most physical phenomena when observed over a very short timescale are probabilistic for masses smaller than a Planck mass and dominated by determinism at or above Planck mass size. Our findings have many implications. For example, we show that the Heisenberg uncertainty principle is rooted in a foundation not valid for rest-mass particles, so the Heisenberg uncertainty principle can say nothing about rest-masses. When re-formulated based on a foundation compatible with a new momentum that is also compatible with rest-masses, we obtain a re-defined Heisenberg principle that seems to become a certainty principle in the special case of a Planck mass particle. Furthermore, we show that the Planck mass particle is linked to gravity and that we can easily detect the Planck scale from gravity observations. The Planck mass particle is unique as it only lasts the Planck time, and in that very short time period it can only be observed directly from itself, and it therefore closely linked to absolute rest.

Recently emerging data-driven citizen sciences need to harness increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks and example models and examine its computational complexity for interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates quality of observation in terms of accuracy and reproducibility, ranging between subjectivity, inter-subjectivity, and objectivity. Next, we examine the database's algebraic and topological structure in relation to informational complexity measures, and evaluate its computational complexities with respect to exhaustive optimization. Conjectures of criticality are obtained on self-organizing processes of observation and dynamical model development. Example analysis is demonstrated with the use of biodiversity assessment database, the process that inevitably involves human subjectivity for the management in open complex systems.

We investigate a possible reduction mechanism from (bosonic) Quantum Field Theory (QFT) to Quantum Mechanics (QM), in a way that could explain the apparent loss of degrees of freedom of the original theory in terms of quantum information in the reduced one. This reduction mechanism consists mainly in performing an ansatz on the boson field operator, which takes into account quantum foam and non-commutative geometry. Through the reduction mechanism, QFT reveals its hidden internal structure, which is a quantum network of maximally entangled multipartite states. In the end, a new approach to the quantum simulation of QFT is proposed through the use of QFT's internal quantum network

Deep Neural Network (DNN) inference demands substantial computing power, resulting in significant energy consumption. A large number of negative output activations in convolution layers are rendered zero due to the invocation of the ReLU activation function. This results in a substantial number of unnecessary computations that consume significant amounts of energy. This paper presents ECHO: Energy-efficient Computation Harnessing Onilne Arithmetic - A MSDF-based accelerator for DNN inference, designed for computation pruning, utilizing an unconventional arithmetic paradigm known as online/ most-significant digit first (MSDF) arithmetic which performs computations in a digit-serial manner. The MSDF digital serial computation of online arithmetic enables overlapped computation of successive operations leading to substantial performance improvements. The online arithmetic, coupled with a negative output detection scheme, facilitates early and precise recognition of negative outputs. This, in turn, allows for the timely termination of unnecessary computations, resulting in a reduction of energy consumption. The implemented design has been realized on the Xilinx Virtex-7 VU3P FPGA and subjected to a comprehensive evaluation through a rigorous comparative analysis involving widely used performance metrics. Experimental results demonstrate promising power and throughput improvements compared to contemporary methods. In particular, the proposed design achieved an average improvement in power consumption of up to 81%, 82.9%, and 40.6% for VGG-16, ResNet-18, and ResNet-50 workloads compared to the conventional bit-serial design, respectively. Furthermore, significant average speedups of 2.39×, 2.6×, and 2.42 were observed when comparing the proposed design to conventional bit-serial designs for VGG-16, ResNet-18, and ResNet-50 models respectively.

Machine learning (ML) based on neural network (NN) facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver, called realFluidReactingNNFoam, under development at University of Perugia in OpenFOAM with NN algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained NN models. Currently, NN models are trained against data generated using the Peng-Robinson equation of state (PR-EoS) assuming mixture frozen temperature. The OpenFOAM solver, where needed, calls the NN models in each grid-cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for the thermophysical properties is achieved via the “Neural Network Inference in C made Easy” (NNICE) library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the PR-EoS and the Chung transport models. First, the development of a real-fluid model (RFM) with artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational ef-ficiency. Results of the overall implementation indicate that the combined OpenFOAM and ML approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.

In this work we develop a new algorithm for efficient computation of the Voigt/complex error function. In particular, in this approach we propose a two-domain scheme where number of the grid-points is dependent on the input parameter $y$. The error analysis we performed shows that the MATLAB implementation meets the requirements for radiative transfer applications involving the HITRAN spectroscopic database. The run-time test shows that this MATLAB implementation provides rapid computation especially at smaller range of the parameter x.

Zinc oxide (ZnO) as a semiconductor in its crystalline or amorphous form is still a promised material, especially under isobaric and isothermal ensembles. In this work, Parallel and Equilibrium Molecular Dynamics and DL_POLY_4 software are employed to predict the relationship between the behavior of ZnO chemical bonds and the phase transition literatures, using correlation function g(r) of Zn-Zn, Zn-O, and O-O pairs. Our system is composed of 5832 atoms of ZnO rocksalt structure (2916 atoms of Zn2+ and 2916 atoms of O2-), under the temperature of 300 (K) and the range of pressure 0-400 (GPa). The lengths of ZnO bonds, the standard error, standard deviation, the maximum of g(r), and the percentage of the variation of the bonds are analyzed. The interatomic interactions are modeled by the potential of Buckingham for short-range and Coulomb for long-range interactions. The calculations were run on the RAVEN Supercomputer of Cardiff University (UK). Our data are mostly in the vicinity of available information of bonds lengths; the rest can be deduced from the pressure of phase transition to use it as a new approach of phase transition confirmation. However, the rest of our results are still a prediction because of no results under extended pressure used in this work.

Every finite simple group P can be generated by two of its elements. Pairs of generators forP are available in the Atlas of finite group representations as (not necessarily minimal) permutation representations P . It is unusual but significant to recognize that a P is a Grothendieck’s dessin d’enfant D and that a wealth of standard graphs and finite geometries G - such as near polygons and their generalizations - are stabilized by a D. In our paper, tripods P − D − G of rank larger than two,corresponding to simple groups, are organized into classes, e.g. symplectic, unitary, sporadic, etc (as in the Atlas). An exhaustive search and characterization of non-trivial point-line configurationsdefined from small index representations of simple groups is performed, with the goal to recognize their quantum physical significance. All the defined geometries G's have a contextuality parameterclose to its maximal value 1.

This paper gives both a general canonical quantum gravity theory and the general canonical quantum gravity theories of the Universe and general black hole, and discovers the relations reflecting symmetric properties of the standard nonlinear gravitational Lagrangian, which are not relevant to any concrete metric models. This paper concretely shows the general commutation relations of the general gravitational field operators and their zeroth, first, second and third style, respectively, of high order canonical momentum operators for the general nonlinear system of the standard gravitational Lagrangian, and then has finished all the four styles of the canonical quantization of the standard gravity.

The theory of the continuous two-dimensional (2D) Fourier Transform in polar coordinates has been recently developed but no discrete counterpart exists to date. In the first part of this two-paper series, we proposed and evaluated the theory of the 2D discrete Fourier Transform (DFT) in polar coordinates. The theory of the actual manipulated quantities was shown, including the standard set of shift, modulation, multiplication, and convolution rules. In this second part of the series, we address the computational aspects of the 2D DFT in polar coordinates. Specifically, we demonstrate how the decomposition of the 2D DFT as a DFT, Discrete Hankel Transform (DHT) and inverse DFT sequence can be exploited for efficient code. We also demonstrate how the proposed 2D DFT can be used to approximate the continuous forward and inverse Fourier transform in polar coordinates in the same manner that the 1D DFT can be used to approximate its continuous counterpart.

The inability to delineate a unified physical ontology that accounts simultaneously for the laws of special relativity and the results of quantum experiments has been a defining problem in physics for more than 100 years. This analysis addresses the problem by positing an ontic, mixed ontology composed of a “discrete” 4D spacetime and a physical, ultra-high dimensional (3 x N) “Planck Space.” Together, Planck Space and the three spatial dimensions of 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the “Dual Ontology”). Critically, the Dual Ontology’s structure replaces 1) the continuous, differentiable manifold of 4D spacetime with a discrete 4D spacetime and 2) mathematical 3N configuration spaces with a physical (3 x N) Planck Space. Moreover, the Dual Ontology is structurally and dynamically predicated on the one-to-one mapping and identity between the discrete spatial units that simultaneously form 4D spacetime and Planck Space. The one-to-one mapping and identity of the discrete spatial units support an integrated quantum dynamics based upon the dynamic evolution of single and N-body quantum states in 4D spacetime in full compliance with the laws of special relativity and the instantaneous collapse of all quantum states in an ontic Planck Space, where special and general relativity and more generally, 4D spacetime’s laws of physics, do not apply.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

The role of fractional calculus in circuit systems has received increased attention in recent years. In order to evaluate the effectiveness of time-domain calculation methods in the analysis of fractional-order piecewise smooth circuit systems, an experimental prototype is developed and the effects of three typical calculation methods in different test scenarios are compared and studied in this paper. It is proved that Oustaloup’s rational approximation method usually overestimates the peak-to-peak current and brings in pulse-voltage phenomenon in piecewise smooth test scenarios, while the results of two iterative recurrence-form numerical methods are in good agreement with the experimental results. The study results are dedicated to provide a reference for efficiently deploying calculation methods in fractional-order piecewise smooth circuit systems. Some quantitative analysis results are concluded in this paper.

Often, when solving forward, inverse or data assimilation problems, only a part of the solution is needed. As a model, we consider the stationary diffusion problem. We demonstrate an algorithm that can compute only a part or a functional of the solution, without calculating the full inversion operator and the complete solution. It is a well-known fact about partial differential equations that the solution at each discretisation point depends on the solutions at all other discretisation points. Therefore, it is impossible to compute the solution only at one point, without calculating the solution at all other points. The standard numerical methods like a conjugate gradient or Gauss elimination compute the whole solution and/or the complete inverse operator. We suggest a method which can compute the solution of the given partial differential equation 1) at a point; 2) at few points; 3) on an interface; or a functional of the solution, without computing the solution at all points. The required storage cost and computational resources will be lower as in the standard approach. With this new method, we can speed up, for instance, computation of the innovation in filtering or the likelihood distribution, which measures the data misfit (mismatch). Further, we can speed up the solution of the regression, Bayesian inversion, data assimilation, and Kalman filter update problems. Applying additionally the hierarchical matrix approximation, we reduce the cubic computational cost to almost linear $\mathcal{O}(k^2n \log^2 n)$, where $k\ll n$ and $n$ is the number of degrees of freedom. Up to the hierarchical matrix approximation error, the computed solution is exact. One of the disadvantages of this method is the need to modify the existing deterministic solver.

In this paper, the connections between quantum non-locality and permutation symmetries are explored. This includes two types of symmetries: permutation across a superposition and permutation of qubits in a quantum system. An algorithm is proposed for nding the separability class of a quantum state using a method based on factorizing an arbitrary multipartite state into possible partitions, cyclically permuting qubits of the vectors in a superposition to check which separability class it falls into and thereafter using a reduced density-matrix analysis of the system is proposed. For the case of mixed quantum states, conditions for separability are found in terms of the partial transposition of the density matrices of the quantum system. One of these conditions turns out to be the Partial Positive Transpose (PPT) condition. A graphical method for analyzing separability is also proposed. The concept of permutation of qubits is shown to be useful in de ning a new entanglement measure in the `engle'.

We present a model of space that considers it to be a quantized dynamical entity which is a component of the universe along with matter and radiation. The theory is used together with thermodynamic data to provide a new view of cosmic evolution and an insight into the nature of dark energy and dark matter. Space is made up of energy quanta. The universe started from an atomic size volume at very high temperature and pressure near the Planck epoch. Upon expansion and cooling, phase transitions occurred resulting in the formation of radiation, fundamental particles, and matter. These nucleate and grow into stars, galaxies, and clusters. From a phase diagram of cosmic composition, we obtained a correlation between dark energy and the energy of space. Using the Friedmann equations, data from WMAP studies of the composition of the universe at 3.0 x 10⁵ (a=5.25 x 10^-2) years and at present (a=1), are well fitted by our model with an equation of state parameter, w= -0.7. The accelerated expansion of the universe, starting at about 7 billion years, determined by BOSS measurements, also correlates well with the dominance of dark energy at 7.25 x 10⁹ years ( a= 0.65). The expansion can be attributed to Quintessence with a space force arising from a quantum space field. From our phase diagram, we also find a correlation suggesting that dark matter is a plasma form of matter similar to that which existed during the photon epoch immediately prior to recombination. Our Quantum Space Model leads to a universe which is homogeneous and isotropic without the need for inflation. The thermodynamics of expansion is consistent with BOSS data that show the process to be adiabatic and the rate of expansion decelerating during the first 6 billion years after the Big Bang. However, it became non-adiabatic and accelerating thereafter. This implies an influx of energy from a source outside the universe; it warrants consideration as a possible factor in the accelerated expansion of the universe.

The paper considers the symmetries of a bit of information corresponding to one, two or three qubits of quantum information and identifiable as the three basic symmetries of the Standard model, U(1), SU(2), and SU(3) accordingly. They refer to “empty qubits” (or the free variable of quantum information), i.e. those in which no point is chosen (recorded). The choice of a certain point violates those symmetries. It can be represented furthermore as the choice of a privileged reference frame (e.g. that of the Big Bang), which can be described exhaustively by means of 16 numbers (4 for position, 4 for velocity, and 8 for acceleration) independently of time, but in space-time continuum, and still one, 17th number is necessary for the mass of rest of the observer in it. The same 17 numbers describing exhaustively a privileged reference frame thus granted to be “zero”, respectively a certain violation of all the three symmetries of the Standard model or the “record” in a qubit in general, can be represented as 17 elementary wave functions (or classes of wave functions) after the bijection of natural and transfinite natural (ordinal) numbers in Hilbert arithmetic and further identified as those corresponding to the 17 elementary of particles of the Standard model. Two generalizations of the relevant concepts of general relativity are introduced: (1) “discrete reference frame” to the class of all arbitrarily accelerated reference frame constituting a smooth manifold; (2) a still more general principle of relativity to the general principle of relativity, and meaning the conservation of quantum information as to all discrete reference frames as to the smooth manifold of all reference frames of general relativity. Then, the bijective transition from an accelerated reference frame to the 17 elementary wave functions of the Standard model can be interpreted by the still more general principle of relativity as the equivalent redescription of a privileged reference frame: smooth into a discrete one. The conservation of quantum information related to the generalization of the concept of reference frame can be interpreted as restoring the concept of the ether, an absolutely immovable medium and reference frame in Newtonian mechanics, to which the relative motion can be interpreted as an absolute one, or logically: the relations, as properties. The new ether is to consist of qubits (or quantum information). One can track the conceptual pathway of the “ether” from Newtonian mechanics via special relativity, via general relativity, via quantum mechanics to the theory of quantum information (or “quantum mechanics and information”). The identification of entanglement and gravity can be considered also as a ‘byproduct” implied by the transition from the smooth “ether of special and general relativity’ to the “flat” ether of quantum mechanics and information. The qubit ether is out of the “temporal screen” in general and is depicted on it as both matter and energy, both dark and visible.

A new framework in quantum chemistry has been proposed recently ("An approach to first principles electronic structure calculation by symbolic-numeric computation'' by A. Kikuchi). It is based on the modern technique of computational algebraic geometry, viz. the symbolic computation of polynomial systems. Although this framework belongs to molecular orbital theory, it fully adopts the symbolic method. The analytic integrals in the secular equations are approximated by the polynomials. The indeterminate variables of polynomials represent the wave-functions and other parameters for the optimization, such as atomic positions and contraction coefficients of atomic orbitals. Then the symbolic computation digests and decomposes the polynomials into a tame form of the set of equations, to which numerical computations are easily applied. The key technique is Grröbner basis theory, by which one can investigate the electronic structure by unraveling the entangled relations of the involved variables. In this article, at first, we demonstrate the featured result of this new theory. Next, we expound the mathematical basics concerning computational algebraic geometry, which are necessitated in our study. We will see how highly abstract ideas of polynomial algebra would be applied to the solution of the definite problems in quantum mechanics. We solve simple problems in "quantum chemistry in algebraic variety'' by means of algebraic approach. Finally, we review several topics related to polynomial computation, whereby we shall have an outlook for the future direction of the research.

Quantum chaos is presented as a paradigm of information processing by dynamical systems at the bottom of the range of phase-space scales. Starting with a brief review of classical chaos as entropy flow from micro- to macro-scales, I argue that quantum chaos came as an indispensable rectification, removing inconsistencies related to entropy in classical chaos: Bottom-up information currents require an inexhaustible entropy production and a diverging information density in phase space, reminiscent of Gibbs' paradox in Statistical Mechanics. It is shown how a mere discretization of the state space of classical models already entails phenomena similar to hallmarks of quantum chaos, and how the unitary time evolution in a closed system directly implies the “quantum death” of classical chaos. As complementary evidence, I discuss quantum chaos under continuous measurement. Here, the two-way exchange of information with a macroscopic apparatus opens an inexhaustible source of entropy and lifts the limitations implied by unitary quantum dynamics in closed systems. The infiltration of fresh entropy restores permanent chaotic dynamics in observed quantum systems. Could other instances of stochasticity in quantum mechanics be interpreted in a similar guise? Where observed quantum systems generate randomness, that is, produce entropy without discernible source, could it have infiltrated from the macroscopic meter? This speculation is worked out for the case of spin measurement.

In this work, we present some results related with the issue of the Loop Quantum Black Holes (LQBH) thermodynamics by the use of the tunneling radiation formalism. The information loss paradox is also discussed in this context, where we have considered the influence of back reaction effects.

We formulate a more straightforward, symmetry-based techniques in manually computing the determinant of any n×n matrix by revisiting Dodgson’s condensation method and strategically applying elementary row operations and the definition and properties of determinants. The result yields a more simplified mnemonical algorithm than the usual reduction by elementary row operations and co-factor expansion and with minimal terminal divisions performed compared to Dodgson’s internal divisions.

We derive a quantum speed limit for unitary evolution for the case of mixed quantum states using the stronger uncertainty relation for mixed quantum states. This bound can be optimized over different choices of Hermitian operators for a better bound. We illustrate this with some examples and show its better performance with respect to three existing bounds for mixed quantum states.

There is a formal analogy between the evolution of the universe, when this is seen as a trajectory in the minisuperspace, and the worldline followed by a test particle in a curved spacetime. The analogy can be extended to the quantum realm, where the trajectories are transformed into wave packets that give us the probability of finding the universe or the particle in a given point of their respective spaces: the spacetime in the case of the particle and the minisuperspace in the case of the universe. The wave function of the spacetime and the matter fields, all together, can then be seen as a super-field that propagates in the minisuperspace and the so-called third quantisation procedure can be applied in a parallel way as the second quantisation procedure is performed with a matter field that propagates in the spacetime. The super-field can thus be interpreted as made up of universes propagating, i.e. evolving, in the minisuperspace. The analogy can also be used in the opposite direction. The way in which the semiclassical state of the universe is obtained in quantum cosmology allows us to obtain, from the quantum state of a field that propagates in the spacetime, the geodesics of the underlying spacetime as well as their quantum uncertainties or dispersions. This might settle a new starting point for a different quantisation of the spacetime.

The spin-torsion theory is a gauge theory approach to gravity that expands upon Einstein's general relativity (GR) by incorporating the spin of microparticles. In this study, we further develop the spin-torsion theory to examine spherically symmetric and static gravitational systems that involve free-falling macroscopic particles. We posit that the quantum spin of macroscopic matter becomes noteworthy on cosmic scales. We further assume that the Dirac spinor and Dirac equation adequately capture all essential physical characteristics of the particles and their associated processes. A crucial aspect of our approach involves substituting the constant mass in the Dirac equation with a scale function, allowing us to establish a connection between quantum effects and the scale of gravitational systems. This mechanism ensures that the quantum effect of macroscopic matter is scale-dependent and diminishes locally, a phenomenon not observed in microparticles. For any given matter density distribution, our theory predicts an additional quantum term, the quantum potential energy (QPE), within the mass expression. QPE induces time dilation, distance contraction, and thus mimics a gravitational well. When applied to cosmology, our theory yields a static cosmological model. The QPE serves as a counterpart to the cosmological constant introduced by Einstein to balance gravity in his static cosmological model. The QPE also offers a plausible explanation for the origin of the Hubble redshift (traditionally attributed to the universe's expansion). The predicted luminosity distance-redshift relation aligns remarkably well with SNe Ia data from the cosmological sample of SNe Ia. In the context of galaxies, QPE functions as the equivalent of dark matter. The predicted circular velocities align well with the rotation curve data from the SPARC (Spitzer Photometry and Accurate Rotation Curves database) sample. Importantly, our conclusions in this paper are reached through a conventional approach, with the sole assumption of the quantum effects of macroscopic matter on large scales, without the need for additional modifications or assumptions.

We study the performance of a classical and quantum magnetic Otto cycle with a quantum dot as a working substance using the Fock-Darwin model with the inclusion of the Zeeman interaction. Modulating an external/perpendicular magnetic field, we found in the classical approach an oscillating behavior in the total work that is not perceptible under the quantum formulation. Also, we compare the work and efficiency of this system for different regions of the Entropy, $S(T,B)$, diagram where we found that the quantum version of this engine always shows a reduced performance in comparison to his classical counterpart.

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

(3+1)D quantum mechanics is a four-dimensional quantum formalism, which is developed neither in a Minkowski spacetime, nor in a purely Hilbert space and not its metric is Lorentzian. This quantum mechanics also yields the quantum Einstein field equation in (3+1)D quantum spacetime. Every observable (3+1)D quantum spacetime can be found to contain internally hidden opposite spin quantum state, that results Supersymmetry. Closed continuous mappings of this hidden quantum state immediately yield more extra hidden dimensions including string. This string is found to be fundamentally eleven-dimensional (rather than lower dimensional) by nature within this quantum spacetime, which is necessarily a natural phenomenon of (3+1)D Quantum Mechanics.

This research focuses on exploring generalized electron emissions in one-dimensional conducting materials, specifically examining field emission and thermionic emission. Additionally, the study investigates one-dimensional thermal radiation. The current density is derived for both one-dimensional thermionic emission and one-dimensional field emission. A generalized current density in one dimension is devised, taking into account arbitrary accelerating fields and temperature conditions. The formula for the one-dimensional current density is depicted with accelerating field and temperature across various work function materials. The current is also represented as a function of both temperature and electric field for various temperatures.

We present detailed models at the hydrodynamics and decoherence phenomena observed in ideal quantum gases. The model is expressed in terms of a quantum field theory, where the action involves both hydrodynamic and decoherence terms. The density-current relation is derived, and the current Green function is introduced. The effective action for two fields is presented, and the physical field is defined in terms of the bare action. Identities such as CTP symmetry, transposition, and the Ward identity are discussed. The Green function and its inverse for different components are also explored. These notes provide a comprehensive understanding of the theoretical framework for studying the hydrodynamics and decoherence of ideal quantum gases.

Moore’s law will cease to be relevant once transistors are the size of electrons. Once this occurs, we will no longer be able to use silicon for technological advancement. However, breakthroughs will continue in the form of a sixth paradigm: a 3D molecular computing model such as quantum comput- ing. Some of the promising areas of quantum computing researched in this study are (i) algorithmic advancements - such as Shor’s, Grover’s, error correction, fault tolerance and other new algorithms; (ii) hardware - such as the quantum system architecture, circuit models, entanglement, superposition and no-cloning qubits; (iii) market segments - such as finance, healthcare, and chemistry; (iv) the supremacy established over classical computing; and (v) areas of applicability. Significant progress has been made in these areas in recent decades, and they have garnered immense interest, attention and the funding required to make good progress.

Particles in classical physics are distinguishable objects, which can be picked out individually on the basis of their unique physical properties. By contrast, in quantum mechanics the standard view is that particles of the same kind (``identical particles'') are completely indistinguishable from each other. This standard view is problematic: Particle indistinguishability is irreconcilable not only with the very meaning of ``particle'' in ordinary language and in classical physical theory, but also with how this term is used in the practice of present-day physics. Moreover, the indistinguishability doctrine prevents a smooth transition from quantum particles to what we normally understand by ``particles'' in the classical limit of quantum mechanics. Elaborating on earlier work, we here discuss an alternative to the standard view that avoids these and similar problems. As it turns out, this alternative approach connects to recent discussions in quantum information theory concerning the question of when identical particles can be considered to be entangled.

A single qubit may be represented on the Bloch sphere or similarly on the 3-sphere S3. Our goal is to dress this correspondence by converting the language of universal quantum computing (UQC) to that of 3-manifolds. A magic state and the Pauli group acting on it define a model of UQC as a positive operator-valued measure (POVM) that one recognizes to be a 3-manifold M3. More precisely, the d-dimensional POVMs defined from subgroups of finite index of the modular group PSL(2, Z) correspond to d-fold M3- coverings over the trefoil knot. In this paper, one also investigates quantum information on a few ‘universal’ knots and links such as the figure-of-eight knot, the Whitehead link and Borromean rings, making use of the catalog of platonic manifolds available on the software SnapPy. Further connections between POVMs based UQC and M3’s obtained from Dehn fillings are explored.

A single qubit may be represented on the Bloch sphere or similarly on the $3$-sphere $S^3$. Our goal is to dress this correspondence by converting the language of universal quantum computing (uqc) to that of $3$-manifolds. A magic state and the Pauli group acting on it define a model of uqc as a POVM that one recognizes to be a $3$-manifold $M^3$. E. g., the $d$-dimensional POVMs defined from subgroups of finite index of the modular group $PSL(2,\mathbb{Z})$ correspond to $d$-fold $M^3$- coverings over the trefoil knot. In this paper, one also investigates quantum information on a few \lq universal' knots and links such as the figure-of-eight knot, the Whitehead link and Borromean rings, making use of the catalog of platonic manifolds available on SnapPy. Further connections between POVMs based uqc and $M^3$'s obtained from Dehn fillings are explored.

Some physicists surmise that gravity lies outside of quantum mechanics. Thus theories like the standard semiclassical theory of quantum to gravity coupling (that of Rosenfeld and Møller) are possible real models of interaction, rather than a mere approximation of a theory of quantum gravity. Unfortunately, semiclassical gravity creates inconsistencies such as superluminal communication. Alternatives by authors such as Diósi, Martin, Penrose, and Wang often use the term 'stochastic' to set themselves apart from the standard semiclassical theory. These theories couple to fluctuations caused by for instance continuous spontaneous localization, hence the term 'stochastic'. This paper looks at stochastic gravity in the framework of a class of emergent or ontological quantum theories, such as those by Bohm, Cetto, and de Broglie. It is found that much or all of the trouble in connecting gravity with a microscopic system falls away, as Einstein's general relativity is free to react directly with the microscopic beables. The resulting continuous gravitational wave radiation by atomic and nuclear systems does not, in contrast to Einstein's speculation, cause catastrophic problems. The small amount of energy exchanged by gravitational waves may have measurable experimental consequences. A very recent experiment by Vinante et al. performed on a small cantilever at mK temperatures shows a surprising non-thermal noise component, the magnitude of which is consistent with the stochastic gravity coupling explored here.

Entanglement is at the heart of quantum technologies such as quantum information and quantum metrology. Providing larger quantum Fisher information (QFI), entangled systems can be better resources than separable systems in quantum metrology. QFI topic is a very active research area and it has many possible usage areas in quantum information domain. In this study, we review quantum Fisher information research with both from theoritical and application perspective.

Magnetic quantum oscillations (MQO) are traditionally applied to investigate the electronic structure of metals. In layered quasi-two-dimensional (Q2D) materials the MQO have several qualitative features giving additional useful information, provided their theoretical description is developed. Within the framework of the Kubo formula and the self-consistent Born approximation, we reconsider the phase of beats in the amplitude of Shubnikov oscillations of interlayer conductivity in Q2D metals. We show that the phase shift of beats of the Shubnikov (conductivity) oscillations relative to the de Haas - van Alphen (magnetization) oscillations is larger than expected previously and, under certain conditions, can reach the value of π/2, as observed experimentally. We explain the phase inversion of MQO during the 3D - 2D crossover and predict the decrease of relative MQO amplitude of interlayer magnetoresistance in a strong magnetic field, larger than the beat frequency.

In this paper, using the two integers that describe the stationary 2-dimensional wave and the charge quantization along with the balance between the Lorentz force and electrical force, we succeed in deriving the fractional quantum Hall effect and the integer quantum Hall effect; we find that the latter exists as a special case of the former. Moreover, using the derived expression describing the fractional quantum Hall effect, a relationship between the plateau in the resistivity of the sample and the applied magnetic field is obtained. The findings of this model agree well with experimental measurements. Because the two integers that describe the stationary 2-dimensional wave and the charge quantization along with the force balance have concrete physical meanings in this work, we could provide a clear picture of the origin of both the integer quantum Hall effect and the fractional quantum Hall effect.

It is claimed that classical mechanics (CM) in terms of its equivalent Koopman-von Neumann formulation (KvN) is indeed a ’real’ quantum theory since the formalism of quantum mechanics (FQM) does not, nor need it, exclude it as a such. This claim is made manifest by suggesting a common structure of which both KvN and ordinary quantum mechanics (OQM) correspond to unitary representations of, although unitarily inequivalent such. It is shown that OQM is obtained by enforcing the extra physical condition of quantized energy levels, which by itself does not constitute the ’quantum mystery’. It is furthermore shown that KvN in a physically realizable sense contain the hallmark quantum behavior of quantum interference. It is hence concluded that both CM and OQM are subjected to the same foundational issues. As the central part of both KvN and OQM is the introduction of probabilities via FQM it is suggested that the foundational issues regarding FQM are less about FQM itself and more about the concept of probability.