Airbus announced the conclusion of its Quantum Computing Challenge (AQCC) on 10 December, revealing the Machine Learning Reply team in Italy, with its aircraft loading optimisation solution, as the winner.
Airlines try to make the best use of an aircraft’s payload capability to maximise revenue, optimise fuel burn and lower overall operating costs. However, their scope for optimisation can be limited by a number of operational constraints. By creating an algorithm to optimise aircraft cargo loading configurations, taking the operational constraints of payload, centre of gravity, size and shape of the fuselage into account, the winning team proved that optimisation problems can be mathematically modelled and solved through quantum computing.
The winners are set to start working with Airbus experts as early as January next year, to test and benchmark their solution in order to assess how mastering complex calculations can tangibly impact airlines, enabling them, as predicted, to benefit from maximised loading capabilities. With operations being made more efficient, the overall number of flights required could be reduced, having a positive impact on CO2 emissions, and thereby contributing to Airbus’ ambition for sustainable flight.
“The Quantum Computing Challenge is testament to Airbus' belief in the power of the collective, to fully harness and apply quantum computing technology to solve complex optimisation challenges facing our industry today," commented Grazia Vittadini, Airbus Chief Technology Officer. "By looking at how emerging technologies can be used to improve aircraft performance and boost innovation, we are addressing the advanced flight physics problems that will redefine how the aircraft of tomorrow are built and flown, and ultimately shape industry, markets and customer experiences for the better."
The AQCC was launched in January 2019 to drive innovation across the full aircraft life-cycle. By developing strong partnerships with the global quantum community, Airbus is taking science out of the lab and into industry, by applying newly-available computing capabilities to real-life industrial cases.