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Acta Physica Sinica

Compressed Quantum Simulation via Quantum Machine Learning

ZHENG Xinbo, Z. Xu, CHEN Yuquan, Zhaokai Li, Xinhua Peng

Published Sep 4, 2026• 0 citations
Publisher

Abstract

量子模拟利用可控的量子系统来模拟一个难以用经典计算机处理的复杂多体量子系统，以应对随多体系统规模指数增长的计算复杂度。本文研究了一种基于量子机器学习的压缩量子模拟方案，通过利用参数化量子线路构建量子自编码器，学习高维希尔伯特空间中量子绝热演化的低维等效表示，并在低维空间中高保真地重构绝热演化过程以实现压缩量子模拟。本文以 9 量子比特二维横场伊辛模型的量子相变检测问题为例，使用 2 量子比特系统对其进行压缩量子模拟，制备基态并测量磁涨落和能量等物理量。结果表明，该方案能在低维希尔伯特空间中高精度复现量子相变行为，所得基态能量的相对误差不超过 2.1%。本研究为在资源有限的量子设备上模拟复杂物理过程提供了新的思路，推动了量子模拟与机器学习的交叉融合。

DOI
10.7498/aps.76.20260593

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量子模拟利用可控的量子系统来模拟一个难以用经典计算机处理的复杂多体量子系统，以应对随多体系统规模指数增长的计算复杂度。本文研究了一种基于量子机器学习的压缩量子模拟方案，通过利用参数化量子线路构建量子自编码器，学习高维希尔伯特空间中量子绝热演化的低维等效表示，并在低维空间中高保真地重构绝热演化过程以实现压缩量子模拟。本文以 9 量子比特二维横场伊辛模型的量子相变检测问题为例，使用 2 量子比特系统对其进行压缩量子模拟，制备基态并测量磁涨落和能量等物理量。结果表明，该方案能在低维希尔伯特空间中高精度复现量子相变行为，所得基态能量的相对误差不超过 2.1%。本研究为在资源有限的量子设备上模拟复杂物理过程提供了新的思路，推动了量子模拟与机器学习的交叉融合。
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Quantum machine learning (QML) offers the potential to revolutionize various computational tasks, particularly in areas requiring complex quantum computations. However, the inherent complexity of quantum states – the superposition and entanglement of quantum bits – poses significant challenges for efficient data representation and manipulation. This paper introduces an innovative quantum state compression algorithm, Adaptive Quantum Machine Learning, designed to address these challenges by dynamically adjusting quantum states during computation. We propose a novel approach that leverages the unique properties of quantum circuits to optimize the representation of quantum states, ultimately leading to improved quantum machine learning performance. The algorithm employs a feedback mechanism to iteratively compress the quantum state, achieving a balance between computational cost and data fidelity. We demonstrate the effectiveness of this approach through a series of simulations and analyses, showcasing significant improvements in both data compression and quantum algorithm efficiency. The paper also discusses potential future extensions and implications for broader QML applications.
This tutorial introduces key concepts in Quantum Machine Learning (QML), covering qubits, gates, entanglement, parameterized circuits, and quantum neural networks (QNNs). It highlights recent advances in quantum-enhanced model compression and quantum architecture search (QAS), which improve QML efficiency and scalability. Attendees will gain hands-on experience with QML implementations on quantum simulators and receive guidance on tools for continued learning. Designed for beginners, the tutorial aims to foster cross-disciplinary innovation at the intersection of quantum computing and AI.
본고에서는 양자역학 기반의 기계학습인 양자 기계학습의 현황과 전망을 조망하고자 한다. 양자역학 기반의 양자컴퓨팅이 보여준 혁신적인 계산속도 개선에 힘입어 기계학습 분야에 양자컴퓨팅 알고리즘을 적용하는 연구는 빅데이터 시대의 도래에 따라 최근 집중적인 관심을 받고 있다. 고전적인 기계학습 알고리즘들에 양자컴퓨팅을 접목하여 획기적인 속도개선을 가능하게 하는 알고리즘 연구들과 최초의 상용 양자컴퓨터로 화제가 되고 있는 양자 담금질 알고리즘 등을 중심으로 양자 기계학습의 최신동향과 가능성을 살펴보고자 한다.
This paper explores the integration of Topological Information Theory (TIT) with Quantum Machine Learning (QML) to achieve quantum acceleration and data compression in machine learning. Traditional machine learning relies on explicit parameter tuning, while this integration leverages the inherent structure of topological information theory for adaptive learning. We propose a novel method for utilizing the topological structure of quantum models as a source of noise, optimizing model parameters through topological analysis to improve efficiency and generalization. The core claim is that utilizing this approach will result in enhanced quantum acceleration and data compression compared to traditional machine learning. This work presents a framework for incorporating topological information into the quantum machine learning pipeline, demonstrating a potential paradigm shift in model optimization and efficiency.
在这篇论文，我们基于旋转网络建议长途的量密码学的一个新计划， qubits 在量点的电子旋转存储了。由有条件的法拉第，旋转，单个光子极化测量，和量状态转，为在二个长途的聚会之间的量密码学的最大纠缠贝尔状态被创造。同时，在任意的距离上的有效的量状态转移被联合力量的一种合适的选择在旋转链获得，用旋转记忆，技术改善了。我们也对能也在旋转网络被实现并且发现克隆的这个旋转网络与一致，最佳的忠实为基于纠纷的密码学由一个偷听者完成了的基于克隆的攻击分析计划的安全。