Validation tests of GBS quantum computers give evidence for quantum advantage with a decoherent target

TL;DR

This paper presents scalable statistical validation tests for large Gaussian boson sampling quantum computers, providing evidence for quantum advantage by comparing experimental data with quantum and classical models.

Contribution

The authors develop a high-order, scalable validation method using positive-P phase-space simulations, enabling effective verification of quantum advantage in large GBS experiments.

Findings

Validation tests disprove classical faked data.

Data aligns more closely with a thermalized quantum model.

Evidence suggests quantum advantage with a decoherent target.

Abstract

Computational validation is vital for all large-scale quantum computers. One needs computers that are both fast and accurate. Here we apply precise, scalable, high order statistical tests to data from large Gaussian boson sampling (GBS) quantum computers that claim quantum computational advantage. These tests can be used to validate the output results for such technologies. Our method allows investigation of accuracy as well as quantum advantage. Such issues have not been investigated in detail before. Our highly scalable technique is also applicable to other applications of linear bosonic networks. We utilize positive-P phase-space simulations of grouped count probabilities (GCP) as a fingerprint for verifying multi-mode data. This is exponentially more efficient than other phase-space methods, due to much lower sampling errors. We randomly generate tests from exponentially many high-order, grouped count tests. Each of these can be efficiently measured and simulated, providing a quantum verification method that is hard to replicate classically. We give a detailed comparison of theory with a 144-channel GBS experiment, including grouped correlations up to the largest order measured. We show how one can disprove faked data, and apply this to a classical count algorithm. There are multiple distance measures for evaluating the fidelity and computational complexity of a distribution. We compute these and explain them. The best fit to the data is a partly thermalized Gaussian model, which is neither the ideal case, nor the model that gives classically computable counts. Even with this model, discrepancies of $Z>100$ were observed from some $\chi^2$ tests, indicating likely parameter estimation errors. Total count distributions were much closer to a thermalized quantum model than the classical model, giving evidence consistent with quantum computational advantage for a modified target problem.