Benchmarking highly entangled states on a 60-atom analog quantum simulator

Authors: Adam L. Shaw, Zhuo Chen, Joonhee Choi, Daniel K. Mark, Pascal Scholl, Ran Finkelstein, Andreas Elben, Soonwon Choi, Manuel Endres

arXiv: 2308.07914v1 - DOI (quant-ph)
ALS, ZC, and JC contributed equally

Abstract: Quantum systems have entered a competitive regime where classical computers must make approximations to represent highly entangled quantum states. However, in this beyond-classically-exact regime, fidelity comparisons between quantum and classical systems have so far been limited to digital quantum devices, and it remains unsolved how to estimate the actual entanglement content of experiments. Here we perform fidelity benchmarking and mixed-state entanglement estimation with a 60-atom analog Rydberg quantum simulator, reaching a high entanglement entropy regime where exact classical simulation becomes impractical. Our benchmarking protocol involves extrapolation from comparisons against many approximate classical algorithms with varying entanglement limits. We then develop and demonstrate an estimator of the experimental mixed-state entanglement, finding our experiment is competitive with state-of-the-art digital quantum devices performing random circuit evolution. Finally, we compare the experimental fidelity against that achieved by various approximate classical algorithms, and find that only one, which we introduce here, is able to keep pace with the experiment on the classical hardware we employ. Our results enable a new paradigm for evaluating the performance of both analog and digital quantum devices in the beyond-classically-exact regime, and highlight the evolving divide between quantum and classical systems.

Submitted to arXiv on 15 Aug. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.