基于数字反向透热补偿的量子算法

doi:10.12066/j.issn.1007-2861.2436

摘要/Abstract

摘要：

与传统计算机不同, 量子计算机在计算速度和能耗方面大大优于传统计算机, 被认为是未来具有重大影响力的新型计算模式之一. 目前, 量子绝热算法、变分量子本征求解器(variational quantum eigensolver, VQE)、量子近似优化算法(quantum approximate optimization algorithm, QAOA)是当前含噪声中等规模量子时代有望用来尝试寻找量子优势的重要算法. 以氢气为例的基态能量计算, 展现了量子绝热算法和变分量子本征求解器在量子化学中的应用. 通过利用数字反向透热补偿法加速量子绝热算法, 并利用变分量子本征求解器实现其优化, 用于降低量子线路深度, 提高能量计算的准确性. 随着研究的不断发展, 该基于数字反向透热补偿的量子算法有望应用于数据搜索、材料设计、生物制药等领域, 体现出量子的优越性.

关键词: 量子绝热算法, 变分量子本征求解器, 反向透热补偿, 量子化学

Abstract:

Quantum computing differs significantly from traditional computers, and is far superior in terms of computing speed and energy consumption. Quantum computing is therefore considered to be one of the new methods with disruptive effects in the future. Currently, quantum adiabatic algorithms, variational quantum eigensolvers (VQEs), and quantum approximate optimization algorithms (QAOAs) are important algorithms that are expected to achieve quantum advantages in the current noisy medium-scale quantum era. In this paper, the calculation of the ground state energy of hydrogen gas is considered as an example to demonstrate the application of the quantum adiabatic algorithm and variational quantum eigensolver in quantum chemistry. The quantum adiabatic algorithm is accelerated using the digital counter-diabatic driving algorithm, and the optimal solution is realized using the variational quantum eigensolver. This helps to reduce the depth of the quantum circuit and improve the accuracy of energy calculation. With this development, the digital counter-diabatic driving quantum algorithm will be extended to the applications in data search, material design, biopharmaceuticals, and so on, demonstrating the advantages in quantum applicability.

Key words: quantum adiabatic algorithm, variational quantum eigensolver (VQE), counter- diabatic driving, quantum chemistry

中图分类号:

O413

王佳楠, 丁泳程, 郝敏佳, 陈玺. 基于数字反向透热补偿的量子算法[J]. 上海大学学报(自然科学版), 2022, 28(5): 883-895.

WANG Jianan, DING Yongcheng, HAO Minjia, CHEN Xi. Digital counter-diabatic driving quantum algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(5): 883-895.

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