Airbus Quantum Computing Challenge
Bringing flight physics into the Quantum Era
The long awaited time has finally arrived! The Airbus Quantum Computing Challenge team have shortlisted 5 finalists. The winner will be announced in December 2020.
Airbus experts from engineering and flight physics teamed up with leading academic and industry experts in Quantum Computing to support the evaluation of submitted proposals. (Find out more about the academic and industry representatives here)
The jury members selected five teams for the 2020 final: Capgemini, Machine Learning Reply, Niels Backfisch, Origin Quantum, Universidad de Montevideo with the described proposal.
In the special podcast, listen to the external judges Elham Kashefi and Iordanis Kerenidis, Thierry Botter from Airbus Blue Sky and Lee-Ann Ramcherita from Airbus' Flight Physics department explaining the assessment and the testimonies of the finalists.
Team Capgemini
Julian van Velzen, Olmo Kortenbosch, Philippe Sottocasa, Michiel Boreel, Gautam Jeyakodi & Philippe Abdiche (from left to right)
The design, development, and production of a new aircraft model takes several years and is dependent on trade-offs between many economic factors.
In the quantum Airbus challenge we propose two novel algorithms inspired by HHL’s well-known algorithm for matrix inversion and QSVM. Both algorithms are designed for an order of magnitude performance gains by reducing parameter space in wingbox optimization.
The algorithms are applicable for LSFT, and NISQ quantum computers respectively, therefore, bridging the gap until the technology has matured. The work is a close collaboration between Capgemini’s aviation HPC, and quantum technology team, thereby acknowledging the need for end-to-end know-how for the design and implementation of quantum algorithms.
Although it is currently impossible to perform a full aircraft optimization, due to the complex interaction between components, the proposed submission may ultimately pave the way for multi-modal, full aircraft optimization.
Team Machine Learning Reply
Nicola Massarenti, Giovanni Pilon & Nicola Gugole (from left to right)
In this submission we solve the loading optimization problem of the Airbus Quantum Computing Challenge using a quantum algorithm.
Finding the optimal loading for a plane is a challenging task for classical algorithms, especially because the solution must respect several flight constraints simultaneously. In this work we prove that optimization problems like this one can be mathematically modelled and solved through Quantum computing, offering a new solution path forward.
Team Niels Backfisch
Niels Backfisch
Several authors showed that averaging over n uncorrelated machine learning models reduces their combined error by a factor of n.
The proposed method computes the predictions of exponentially many quantum machine learning models, including all possible uncorrelated models, for the cost of a single model.
With some technical mathematical requirements it even allows the training of such a model for regression. The training finishes with 99% probability within 10 epochs, regardless of the data or the model size.
Therefore the proposed method might decrease the machine learning model’s error by a large factor, growing exponentially with the number of qubits, while only needing to see the data 10 times or less.
Team Origin Quantum
Yongjie Zhao, Weicheng Kong, Zhaoyun Chen, Zhilong Jia, Hui Zhang (from left to right)
Computational Fluid Dynamics is important for its application in aircraft design. As the number of grid points grows, evermore powerful computing resources are needed. In our submission, we propose a quantum-classical hybrid method for accelerating SU2, an open-source software for CFD. Our method is based on the Finite Volume Method. We implement this method by replacing the space integration and the linear solver modules with quantum modules and preserve the iteration (time integration) process. The time complexity of our method is O(polylog N), poly-logarithmically dependent on the number of grid points. Our quantum algorithm is implemented in SU2 with submodules written by a quantum programming language QPanda. We assert that the fault-tolerant quantum computing and the quantum random access memory are necessary for showing the quantum advantage for this algorithm.
Team Universidad de Montevideo
Rafael Sotelo, Gerardo Beltrame, Martín Machín, Laura Gatti, Ignacio Méndez, Maximiliano Stock, Joaquín Fernández, Diego Gibert, Juan-Diego Orihuela, José-Pedro Algorta (from left to right)
The problem of determining the optimal loading strategy for packing merchandise in vehicles has been addressed using different techniques like dynamic or genetic programming. Inspired in the well-known Knapsack Problem, our solution aims at determining the optimal configuration of maximizing the loading of a cargo airplane -subject to constraints- using quantum algorithms.
Our approach is based on the Variational Quantum Eigensolver (VQE) algorithm. Understanding the complexity of classical computational methods we aim to find the quantum advantage provided by the VQE in a hybrid solution.
As the number of packages to be transported grows the number of possible combinations increases posing a serious challenge to be solved by classical means.
Based on the complexity order of the problem we project the number of necessary qubits to solve it and the feasibility of running the algorithm in a quantum computer currently available or in the near future.
With traditional computers gradually approaching their limits, the quantum computer promises to deliver a new level of computational power. As an active user of advanced computing solutions, Airbus is at the forefront of a paradigm shift in the computing world exploring how quantum computing could solve key questions for the aerospace industry, and forever alter how aircraft are built and flown.
To take us one step further, Airbus launched a global quantum computing competition in January 2019, challenging experts and enthusiasts in the field to join forces with the company for a Quantum Era in aerospace.
The Airbus Quantum Computing Challenge (AQCC) addresses aerospace flight physics problems developed by company experts. Airbus is providing the quantum computing community with a unique opportunity to test and assess the newly-available computing capabilities to solve some of our most difficult and complex problems, and in doing so, further legitimise and fuel progress of this technology.
The challenge puts forward five distinct flight physics problems with varying degrees of complexity, ranging from a simple mathematical question to a global flight physics problem.
It is open to the whole scientific community of experts, researchers, start-ups, academics and will lay the ground for the ultimate shift to a Quantum era in aerospace.
Flight physics, the broad denomination of all scientific and engineering aspects related to the flight of aircraft, is at the heart of Airbus’ business. The topic affects virtually all aspects of an aircraft’s life: from design to operation, from the quality of movement through the air to the revenue stream of airlines. The full lifecycle features many computationally difficult problems. Although computational methods and approaches exist today to address these challenges, Airbus, in its drive for innovation and improvement, is constantly seeking to revolutionise capabilities to provide innovative products that fly!
Airbus is fuelling this transformation by laying down five challenges faced in aircraft design and in-service optimisation for Quantum Computing experts and enthusiasts (post-graduate students, PhDs, academics, researchers, start-ups, or professionals in the field) to resolve using quantum computing and embark on this transformation journey collaboratively.
Solutions will enable Airbus to assess how this burgeoning computational technology could be included or even replace other high-performance computational tools that, today, form the cornerstone of aircraft design.
Read the five problem statements below.
Specific conditions related to the use of the entry are detailed in the Terms and Conditions available here.
Problem Statements
Problem Statement 1: Aircraft Climb Optimisation
Aircraft follow several flight phases during their ‘mission’ from take-off to landing. Cruise is the longest segment and is considered most important from a fuel and time optimisation perspective. Yet for the ever-increasing volume of short-haul flights, climb and descent are more critical. Fuel optimisation during these segments is very valuable for airlines. This problem focuses on the climb and how quantum computing can be applied to arrive at a low-cost index (the relative cost of time and fuel), which is central to climb efficiency.
Problem Statement 2: Computational Fluid Dynamics
The efficiency of aircraft design relies heavily on the aircraft’s overall aerodynamic shape. This design is performed using Computational Fluid Dynamics (CFD), demonstrate airflow behaviour around the aircraft and reveal the aerodynamic forces acting on its surfaces. However, accurate CFD simulations are a resource- and time-consuming task. This challenge aims to show how established CFD simulations can be run using a quantum computing algorithm or in a hybrid quantum-traditional way for faster problem solving and how the algorithm can scale in line with the problem complexity including computational resources.
Problem Statement 3: Quantum Neural Networks for Solving Partial Differential Equations
Solving Partial Differential Equations (PDEs) is a major challenge when solving aerodynamic problems. Today, their resolution requires complex numerical schemes and high computational costs. Traditionally PDEs were solved in a deterministic manner using numerical methods. Recently, neural networks – deep-learning-based algorithms – have been developed to solve coupled PDEs. These networks compute the time and space derivatives of a PDE. The proposed challenge is to augment this new approach for aerodynamic problems with quantum capabilities.
Can you establish an approach useful in aviation and potentially the wider digital community?
Problem Statement 4: Wingbox Design Optimisation
Given the limitations of traditional computing, the aerospace industry faces a challenge in optimising multidisciplinary design. That’s when design configurations such as airframe loads, mass modelling and structural analysis must be simultaneously calculated. This can cause long design lead times, convoluted processes and conservative assessments. Quantum computing offers an alternative path to explore a wider design space by evaluating different parameters simultaneously, thus preserving structural integrity while optimising weight. This balance is particularly important in aircraft wingbox design, where weight optimisation is key to low operating costs and reduced environmental impact.
How do you propose quantum computing could address this complexity?
Problem Statement 5: Aircraft Loading Optimisation
Airlines try to make the best use of an aircraft’s payload capability to maximise revenue, optimise fuel burn and lower overall operating costs. Their scope for optimisation is limited by the aircraft’s operational envelope, which is determined by each mission’s maximum payload capacity, the aircraft’s centre of gravity and its fuselage shear limits. The objective of this challenge is to calculate the optimal aircraft configuration under coupled operational constraints, thus demonstrating how quantum computing can be used for practical problem solving and how it can scale towards more complex issues.
Put your theory to the test and be part of the breakthrough in quantum computing!
What do the winners receive?
Winners will be offered unique opportunities for access to cloud based quantum computing resources to implement their proposed approach and to work collaboratively with our industry experts from the flight physics sector to further adapt their solutions for the industrial aerospace applications.
Throughout the competition we interacted with the QC Community, answering questions and clarifying the problem.
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