Race for Quantum Advantage using Random Circuit Sampling
Sangchul Oh, Sabre Kais

TL;DR
This paper critically compares classical tensor network simulations with quantum processor outputs in random circuit sampling, revealing that classical samples can mimic some statistical properties but differ in key aspects, questioning claims of quantum advantage.
Contribution
It provides a detailed statistical comparison between classical and quantum samples, highlighting limitations of current certification methods for quantum advantage in random circuit sampling.
Findings
Kalachev et al.'s samples pass NIST randomness tests
Classical samples differ from quantum samples in heat map analysis
Kalachev et al.'s samples are statistically closer to quantum samples than Pan et al.'s
Abstract
Random circuit sampling, the task to sample bit strings from a random unitary operator, has been performed to demonstrate quantum advantage on the Sycamore quantum processor with 53 qubits and on the Zuchongzhi quantum processor with 56 and 61 qubits. Recently, it has been claimed that classical computers using tensor network simulation could catch on current noisy quantum processors for random circuit sampling. While the linear cross entropy benchmark fidelity is used to certify all these claims, it may not capture in detail statistical properties of outputs. Here, we compare the bit strings sampled from classical computers using tensor network simulation by Pan et al. [Phys. Rev. Lett. 129, 090502 (2022)] and by Kalachev et al. [arXiv:2112.15083 (2021)] and from the Sycamore and Zuchongzhi quantum processors. It is shown that all Kalachev et al.'s samples pass the NIST random number…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Computational Physics and Python Applications · Quantum Information and Cryptography
