Is the 2025 OTOC(2) quantum echo experiment beyond classical simulation
In plain words
In 2025 Google measured how information spreads and refocuses on a superconducting chip of 103 qubits and argued that classical computers would need vastly longer to reproduce the numbers. Whether improved classical methods can compute these values to the same accuracy is unsettled.
Precise statement
For the second-order out-of-time-order correlator (OTOC(2)) circuits run by Google Quantum AI in 2025 (arXiv:2506.10191), compute the reported observables with error at or below the experimental error bars using classical methods (tensor networks, Pauli-path or Monte Carlo) in time comparable to the experiment, or give complexity-theoretic evidence that no such algorithm exists at that size.
What would settle it
A classical computation reproducing the published OTOC(2) values within error at modest cost, or a hardness argument tied to the experimental parameters.
Status in the literature
Unverified note
Google Quantum AI claimed a large classical-cost gap in 2025 and published a simplified OTOC(2) problem statement for classical challengers (arXiv:2510.19751).