Classically Spoofing System Linear Cross Entropy Score Benchmarking
Andrew Tanggara, Mile Gu, Kishor Bharti

TL;DR
This paper analyzes the classical simulability of the System Linear Cross Entropy Score (sXES), a quantum benchmarking metric, revealing it can be efficiently simulated classically under certain conditions, challenging its use for quantum supremacy.
Contribution
It demonstrates that sXES, a promising quantum benchmarking metric, can be efficiently simulated classically in specific regimes, questioning its effectiveness for quantum advantage claims.
Findings
sXES can be classically simulated efficiently in certain regimes
The hardness assumptions underlying sXES are not fully established
sXES's fundamental distinction from Linear XEB does not guarantee quantum advantage
Abstract
In recent years, several experimental groups have claimed demonstrations of ``quantum supremacy'' or computational quantum advantage. A notable first claim by Google Quantum AI revolves around a metric called the Linear Cross Entropy Benchmarking (Linear XEB), which has been used in many quantum supremacy experiments since. The complexity-theoretic hardness of spoofing Linear XEB, however, depends on the Cross-Entropy Quantum Threshold (XQUATH) conjecture put forth by Aaronson and Gunn, which has been disproven for sublinear depth circuits. In the efforts on demonstrating quantum supremacy by quantum Hamiltonian simulation, a similar benchmarking metric called the System Linear Cross Entropy Score (sXES) holds firm in light of the aforementioned negative result due to its fundamental distinction with Linear XEB. Moreover, the complexity-theoretic hardness of spoofing sXES rests on the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsControl Systems and Identification
