CPQM’s Laboratory for Quantum Information Processing has collaborated with the CDISE supercomputing team “Zhores” to emulate Google’s quantum processor. Reproducing noiseless data following the same statistics as Google’s recent experiments, the team was able to point to a subtle effect lurking in Google’s data. This effect, called a reachability deficit, was discovered by the Skoltech team in its past work . The numerics confirmed that Google’s data was on the edge of a so-called, density-dependent avalanche, which implies that future experiments will require significantly more quantum resources to perform quantum approximate optimization. The results are published in the field’s leading journal Quantum.
From the early days of numerical computing, quantum systems have appeared exceedingly difficult to emulate, though the precise reasons for this remain a subject of active research. Still, this apparently inherent difficulty of a classical computer to emulate a quantum system prompted several researchers to flip the narrative.
Scientists such as Richard Feynman and Yuri Manin speculated in the early 1980s that the unknown ingredients which seem to make quantum computers hard to emulate using a classical computer could themselves be used as a computational resource. For example, a quantum processor should be good at simulating quantum systems, since they are governed by the same underlying principles.