Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage
Xun Gao, Marcin Kalinowski, Chi-Ning Chou, Mikhail D. Lukin, Boaz, Barak, and Soonwon Choi

TL;DR
This paper critically evaluates the Linear Cross-Entropy Benchmark (XEB) for quantum advantage, revealing its limitations and vulnerabilities, and demonstrates that high XEB values can be achieved classically without simulating quantum dynamics.
Contribution
The authors analyze the conditions under which XEB approximates fidelity and show that high XEB scores can be obtained by classical algorithms, exposing limitations of XEB as a quantum advantage benchmark.
Findings
High XEB does not imply faithful quantum simulation.
Classical algorithms can achieve high XEB values efficiently.
XEB's correlation with fidelity depends on specific conditions.
Abstract
Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated with a class of highly entangled many-body wavefunctions. It has been suggested that this approach can be certified with the Linear Cross-Entropy Benchmark (XEB). We critically examine this notion. First, in a "benign" setting where an honest implementation of noisy quantum circuits is assumed, we characterize the conditions under which the XEB approximates the fidelity. Second, in an "adversarial" setting where all possible classical algorithms are considered for comparison, we show that achieving relatively high XEB values does not imply faithful simulation of quantum dynamics. We present an efficient classical algorithm that, with 1 GPU within 2s,…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum many-body systems · Quantum Information and Cryptography
