Low-depth Amplitude Estimation without Really Trying

Standard quantum amplitude estimation algorithms provide quadratic speedup to Monte-Carlo simulations but require a circuit depth that scales as inverse of the estimation error. In view of the shallow depth in near-term devices, the precision achieved by these algorithms would be low. In this article, we bypass this limitation by performing the classical Monte-Carlo method on the quantum algorithm itself, achieving a higher than classical precision using low-depth circuits. We require the quantum algorithm to be weakly biased in order to avoid error accumulation during this process. Our method is parallel and can be as weakly biased as the constituent algorithm in some cases.

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Publication Details

Journal
ACM Transactions on Quantum Computing
Published
2025-07-26
DOI
https://doi.org/10.1145/3748666
Primary Topic
Blind Source Separation Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Low-depth Amplitude Estimation without Really Trying

Patrick Rebentrost, Dinh-Long Vu, Bin Cheng
ACM Transactions on Quantum Computing
Blind Source Separation Techniques
article

Low-depth Amplitude Estimation without Really Trying

Patrick Rebentrost, Dinh-Long Vu, Bin Cheng
article en

Abstract

Standard quantum amplitude estimation algorithms provide quadratic speedup to Monte-Carlo simulations but require a circuit depth that scales as inverse of the estimation error. In view of the shallow depth in near-term devices, the precision achieved by these algorithms would be low. In this article, we bypass this limitation by performing the classical Monte-Carlo method on the quantum algorithm itself, achieving a higher than classical precision using low-depth circuits. We require the quantum algorithm to be weakly biased in order to avoid error accumulation during this process. Our method is parallel and can be as weakly biased as the constituent algorithm in some cases.

ACM Transactions on Quantum ComputingVol. 6(4)
Centre for Quantum Technologies (SG)
Openalex Percentile: Top 79%
Blind Source Separation Techniques
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