Evaluating classical simulations with a quantum processor

TL;DR

This paper uses a quantum annealing processor to evaluate classical tensor-network methods, revealing discrepancies with previous scaling predictions and emphasizing caution in extrapolating classical simulation accuracy for quantum systems.

Contribution

It introduces a novel approach of using a quantum processor as a ground truth to assess classical simulation methods, challenging existing scaling assumptions.

Findings

Quantum annealing provides a reliable ground truth for classical methods.

Classical tensor-network methods underperform compared to previous predictions.

Results highlight the importance of cautious extrapolation in quantum simulation accuracy.

Abstract

As simulations of quantum systems cross the limits of classical computability, both quantum and classical approaches become hard to verify. Scaling predictions are therefore based on local structure and asymptotic assumptions, typically with classical methods being used to evaluate quantum simulators where possible. Here, in contrast, we use a quantum annealing processor to produce a ground truth for evaluating classical tensor-network methods whose scaling has not yet been firmly established. Our observations run contrary to previous scaling predictions, demonstrating the need for caution when extrapolating the accuracy of classical simulations of quantum dynamics. Our results demonstrate that the virtuous cycle of competition between classical and quantum simulations can lend insight in both directions.