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基于含时乘积公式的高效实用哈密顿量模拟

Efficient and practical Hamiltonian simulation from time-dependent product formulas.

作者信息

Bosse Jan Lukas, Childs Andrew M, Derby Charles, Gambetta Filippo Maria, Montanaro Ashley, Santos Raul A

机构信息

Phasecraft Ltd. 77 Charlotte Street, W1T 4PW, London, UK.

School of Mathematics, University of Bristol, Bristol, UK.

出版信息

Nat Commun. 2025 Mar 26;16(1):2673. doi: 10.1038/s41467-025-57580-5.

DOI:10.1038/s41467-025-57580-5
PMID:40140640
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11947201/
Abstract

In this work we propose an approach for implementing time-evolution of a quantum system using product formulas. The quantum algorithms we develop have provably better scaling (in terms of gate complexity and circuit depth) than a naive application of well-known Trotter formulas, for systems where the evolution is determined by a Hamiltonian with different energy scales (i.e., one part is "large" and another part is "small"). Our algorithms generate a decomposition of the evolution operator into a product of simple unitaries that are directly implementable on a quantum computer. Although the theoretical scaling is suboptimal compared with state-of-the-art algorithms (e.g., quantum signal processing), the performance of the algorithms we propose is highly competitive in practice. We illustrate this via extensive numerical simulations for several models. For instance, in the strong-field regime of the 1D transverse-field Ising model, our algorithms achieve an improvement of one order of magnitude in both the system size and evolution time that can be simulated with a fixed budget of 1000 arbitrary 2-qubit gates, compared with standard Trotter formulas.

摘要

在这项工作中,我们提出了一种使用乘积公式实现量子系统时间演化的方法。我们开发的量子算法在门复杂度和电路深度方面,对于演化由具有不同能量尺度的哈密顿量决定的系统(即一部分是“大”的,另一部分是“小”的),相比于直接应用著名的 Trotter 公式,具有可证明更好的扩展性。我们的算法将演化算符分解为简单酉算符的乘积,这些酉算符可直接在量子计算机上实现。尽管与最先进的算法(例如量子信号处理)相比,理论扩展性不是最优的,但我们提出的算法在实践中的性能具有高度竞争力。我们通过对几个模型的广泛数值模拟来说明这一点。例如,在一维横向场伊辛模型的强场区域,与标准 Trotter 公式相比,在固定的 1000 个任意双量子比特门预算下,我们的算法在可模拟的系统大小和演化时间上都实现了一个数量级的提升。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/3495bf5c552c/41467_2025_57580_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/380bf6f8315b/41467_2025_57580_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/b0de9297ec45/41467_2025_57580_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/61e23c82c72f/41467_2025_57580_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/3261db8a7077/41467_2025_57580_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/3495bf5c552c/41467_2025_57580_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/380bf6f8315b/41467_2025_57580_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/b0de9297ec45/41467_2025_57580_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/61e23c82c72f/41467_2025_57580_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/3261db8a7077/41467_2025_57580_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/937d/11947201/3495bf5c552c/41467_2025_57580_Fig5_HTML.jpg

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