Classical Sampling of Random Quantum Circuits with Bounded Fidelity

Kalachev, Gleb; Panteleev, Pavel; Zhou, Pengfei; Yung, Man‐Hong

doi:10.48550/arxiv.2112.15083

Cited by 1 publication

(2 citation statements)

References 14 publications

(42 reference statements)

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“…An analogous approach is pursued by Kalachev et al (2021b), who devise a slicing procedure based on maximizing the norm of partially summed slices to match a targeted fidelity. Similarly to the approach of , this gives rise to batches of correlated probabilities.…”

Section: Simulation Of Random Quantum Circuitsmentioning

confidence: 99%

“…Similarly to the approach of , this gives rise to batches of correlated probabilities. Kalachev et al (2021b) further provide an optimized sampling procedure that minimizes the sampling overhead to a factor of 2. They estimate that this would allow them to sample from a dis-tribution with fidelity 0.2% using 15 months time on a single GPU.…”

Section: Simulation Of Random Quantum Circuitsmentioning

confidence: 99%

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Computational advantage of quantum random sampling

Hangleiter¹,

Eisert²

2022

Preprint

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Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementations of quantum random sampling have arguably surpassed the boundary of what can be simulated on existing classical hardware. In this article, we comprehensively review the theoretical underpinning of quantum random sampling in terms of computational complexity and verifiability, as well as the practical aspects of its experimental implementation using superconducting and photonic devices and its classical simulation. We discuss in detail open questions in the field and provide perspectives for the road ahead, including potential applications of quantum random sampling.

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Section: Simulation Of Random Quantum Circuitsmentioning

confidence: 99%

Section: Simulation Of Random Quantum Circuitsmentioning

confidence: 99%