Lurking in the background of the quest for true quantum supremacy hangs an awkward possibility – hyper-fast number crunching tasks based on quantum trickery might just be a load of hype.
Now, a pair of physicists from École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland and Columbia University in the US have come up with a better way to judge the potential of near-term quantum devices – by simulating the quantum mechanics they rely upon on more traditional hardware.
Their study made use of a neural network developed by EPFL’s Giuseppe Carleo and his colleague Matthias Troyer back in 2016, using machine learning to come up with an approximation of a quantum system tasked with running a specific process.
Known as the Quantum Approximate Optimization Algorithm (QAOA), the process identifies optimal solutions to a problem on energy states from a list of possibilities, solutions that should produce the fewest errors when applied.
“There is a lot of interest in understanding what problems can be solved efficiently by a quantum computer, and QAOA is one of the more prominent candidates,” says Carleo.