Quantum computing research is being applied to tough flight physics problems.
This is even with the small quantum computer systems that are available today. Nextbigfuture believes the pre-error corrected quantum computers that will be available over the next five years could have 1,000 to 10,000 qubits for noisy intermediate scale quantum (NISQ) and for trapped ion quantum computers. D-Wave quantum annealing systems are already testing 5640 qubit systems and should also go to larger and more connected systems.
Airbus has launched a quantum computing challenge in which academics, startups, and other quantum science experts can submit proposals for solving five complex flight-physics problems.
Winners will be offered unique opportunities for hardware access (scheduled for 2020 and after), and to work collaboratively with our industry experts from the flight physics sector.
QC Ware for Quantum Algorithms and Quantumm Solution Partnering
Airbus Ventures funded QC Ware in 2016. QC Ware has offices in Palo Alto. Brian Wang of Nextbigfuture has visited with QC Ware and talked to the CEO Matt Johnson and Head of Business Development Yianni Gamvros.
Airbus challenge problems include tasks that can be solved in minutes with a quantum computer, versus hours with a classical computer, said Matt Johnson, founder and CEO of QC Ware. QC Ware has raised about $8 million in venture funding to date.
QC Ware is developing and implementing quantum algorithms on all of the quantum hardware of partner companies like D-Wave, IBM, Google and others.
QC Ware’s help in understanding how quantum computing could be applied to mathematical calculations in the design phase of aircraft systems and parts. Redesigning parts can be a time-consuming process with classical computers, which can take days to perform calculations.
Airbus and QC Ware found some aerospace calculation were made four times faster with a quantum computer.
Five Problems in the Quantum Computing Challenge
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 legitimize and fuel progress of this technology.
The challenge is open to Quantum Computing experts and enthusiasts (post-graduate students, PhDs, academics, researchers, start-ups, or professionals in the field), and you can participate either as an individual or as a team. To access the technical details of the problem statements please register here.
The submission period ends in October 2019. The assessment period will last until the end of January 2020, following which participants will be informed about the results within the first quarter of 2020.
Problem Statement 1: Aircraft Climb Optimization
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.
Problem Statement 4: Wingbox Design Optimization
Given the limitations of traditional computing, the aerospace industry faces a challenge in optimizing multidisciplinary design. That’s when design configurations such as airframe loads, mass modeling 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 wing box design, where weight optimization is key to low operating costs and reduced environmental impact.
Problem Statement 5: Aircraft Loading Optimization
Airlines try to make the best use of an aircraft’s payload capability to maximize revenue, optimize fuel burn and lower overall operating costs. Their scope for optimization is limited by the aircraft’s operational envelope, which is determined by each mission’s maximum payload capacity, the aircraft’s center 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.