Is quantum computing ready for take-off in aviation?

There’s no doubt about it: demand for air travel is remarkably resilient, with many airports seeing record numbers of passengers.

Whilst Europe remains the world’s top tourist destination, it is also home to half of the world’s most congested airports, according to ACI EUROPE – a challenge growing in size and severity, with geopolitical tensions, adverse weather, and air traffic control capacity shortfalls also limiting overall airspace capacity, often unexpectedly.

Artificial intelligence (AI) has long been considered a potential avenue of optimisation and delay reduction for air traffic flow management (ATFM), with the objective being to quicken safe decision-making through higher levels of streamlining and automation, allowing human operators to focus on safety-critical tasks.

Yet while AI technology is improving leaps and bounds, the complexity of optimising airspace grows with it: and as demand grows, so does the overall size of the problem.

This has led many researchers to question not only whether AI can produce an answer to complex problems, but whether it can do so fast enough to be tactically useful in real operations.