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Main Authors: Kalai, Gil, Rinott, Yosef, Shoham, Tomer
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2404.00935
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author Kalai, Gil
Rinott, Yosef
Shoham, Tomer
author_facet Kalai, Gil
Rinott, Yosef
Shoham, Tomer
contents Considerable effort in experimental quantum computing is devoted to noisy intermediate scale quantum computers (NISQ computers). Understanding the effect of noise is important for various aspects of this endeavor including notable claims for achieving quantum supremacy and attempts to demonstrate quantum error correcting codes. In this paper we use Fourier methods combined with statistical analysis to study the effect of noise. In particular, we use Fourier analysis to refine the linear cross-entropy fidelity estimator. We use both analytical methods and simulations to study the effect of readout and gate errors, and we use our analysis to study the samples of Google's 2019 quantum supremacy experiment.
format Preprint
id arxiv_https___arxiv_org_abs_2404_00935
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Random Circuit Sampling: Fourier Expansion and Statistics
Kalai, Gil
Rinott, Yosef
Shoham, Tomer
Quantum Physics
Computational Complexity
Statistics Theory
Considerable effort in experimental quantum computing is devoted to noisy intermediate scale quantum computers (NISQ computers). Understanding the effect of noise is important for various aspects of this endeavor including notable claims for achieving quantum supremacy and attempts to demonstrate quantum error correcting codes. In this paper we use Fourier methods combined with statistical analysis to study the effect of noise. In particular, we use Fourier analysis to refine the linear cross-entropy fidelity estimator. We use both analytical methods and simulations to study the effect of readout and gate errors, and we use our analysis to study the samples of Google's 2019 quantum supremacy experiment.
title Random Circuit Sampling: Fourier Expansion and Statistics
topic Quantum Physics
Computational Complexity
Statistics Theory
url https://arxiv.org/abs/2404.00935