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如何通过实验评估量子退火的绝热条件。

How to experimentally evaluate the adiabatic condition for quantum annealing.

作者信息

Mori Yuichiro, Kawabata Shiro, Matsuzaki Yuichiro

机构信息

Global Research and Development Center for Business by Quantum-AI Technology (G-QuAT), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1, Umezono, Tsukuba, Ibaraki, 305-8568, Japan.

NEC-AIST Quantum Technology Cooperative Research Laboratory, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8568, Japan.

出版信息

Sci Rep. 2024 Apr 8;14(1):8177. doi: 10.1038/s41598-024-58286-2.

DOI:10.1038/s41598-024-58286-2
PMID:38589470
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11001971/
Abstract

We propose an experimental method for evaluating the adiabatic condition during quantum annealing (QA), which will be essential for solving practical problems. The adiabatic condition consists of the transition matrix element and the energy gap, and our method simultaneously provides information about these components without diagonalizing the Hamiltonian. The key idea is to measure the power spectrum of a time domain signal by adding an oscillating field during QA, and we can estimate the values of the transition matrix element and energy gap from the measurement output. Our results provides a powerful experimental basis for analyzing the performance of QA.

摘要

我们提出了一种用于评估量子退火（QA）过程中绝热条件的实验方法，这对于解决实际问题至关重要。绝热条件由跃迁矩阵元和能隙组成，我们的方法无需对哈密顿量进行对角化即可同时提供有关这些分量的信息。关键思想是在量子退火过程中通过添加振荡场来测量时域信号的功率谱，并且我们可以从测量输出中估计跃迁矩阵元和能隙的值。我们的结果为分析量子退火的性能提供了有力的实验依据。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcbc/11001971/df1bef8a6cfd/41598_2024_58286_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcbc/11001971/93000f1e5b7c/41598_2024_58286_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcbc/11001971/7478f26f70d4/41598_2024_58286_Fig11_HTML.jpg

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本文引用的文献

1
Quantum annealing: an overview.量子退火：概述
Philos Trans A Math Phys Eng Sci. 2023 Jan 23;381(2241):20210417. doi: 10.1098/rsta.2021.0417. Epub 2022 Dec 5.
2
Greedy parameter optimization for diabatic quantum annealing.用于非绝热量子退火的贪婪参数优化
Philos Trans A Math Phys Eng Sci. 2023 Jan 23;381(2241):20210416. doi: 10.1098/rsta.2021.0416. Epub 2022 Dec 5.
3
Adiabatic quantum linear regression.绝热量子线性回归
Sci Rep. 2021 Nov 9;11(1):21905. doi: 10.1038/s41598-021-01445-6.
4
Evaluating Energy Differences on a Quantum Computer with Robust Phase Estimation.利用稳健相位估计在量子计算机上评估能量差异。
Phys Rev Lett. 2021 May 28;126(21):210501. doi: 10.1103/PhysRevLett.126.210501.
5
Electronic structure with direct diagonalization on a D-wave quantum annealer.在D波量子退火器上进行直接对角化的电子结构。
Sci Rep. 2020 Nov 27;10(1):20753. doi: 10.1038/s41598-020-77315-4.
6
Perspectives of quantum annealing: methods and implementations.量子退火的展望：方法与实现
Rep Prog Phys. 2020 May;83(5):054401. doi: 10.1088/1361-6633/ab85b8. Epub 2020 Apr 1.
7
Enhancing quantum annealing performance by a degenerate two-level system.通过简并二能级系统提高量子退火性能。
Sci Rep. 2020 Jan 10;10(1):146. doi: 10.1038/s41598-019-56758-4.
8
Experimental investigation of performance differences between coherent Ising machines and a quantum annealer.相干伊辛机与量子退火器性能差异的实验研究
Sci Adv. 2019 May 24;5(5):eaau0823. doi: 10.1126/sciadv.aau0823. eCollection 2019 May.
9
Solving a Higgs optimization problem with quantum annealing for machine learning.使用量子退火解决机器学习中的 Higgs 优化问题。
Nature. 2017 Oct 18;550(7676):375-379. doi: 10.1038/nature24047.
10
Adiabatic quantum simulation of quantum chemistry.量子化学的绝热量子模拟。
Sci Rep. 2014 Oct 13;4:6603. doi: 10.1038/srep06603.