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Probabilistic simulation of quantum circuits using a deep-learning architecture
Physical Review A ( IF 2.6 ) Pub Date : 2021-09-20 , DOI: 10.1103/physreva.104.032610
Juan Carrasquilla , Di Luo , Felipe Pérez , Ashley Milsted , Bryan K. Clark , Maksims Volkovs , Leandro Aolita

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

中文翻译:

使用深度学习架构对量子电路进行概率模拟

如何使用传统计算资源最好地模拟量子系统的基本问题是凝聚态物质和量子计算的前沿。它影响我们对量子材料的理解和我们模拟量子电路的能力。在这里,我们通过分解广义测量提出了量子动力学的精确公式,该测量将量子状态映射到概率分布,其优点是局部酉动力学和量子通道映射到局部准随机矩阵。这种表示提供了一个通用框架,用于在机器学习中使用最先进的概率模型来模拟量子多体动力学。使用这个框架,我们开发了一种实用算法,使用基于强大神经网络 ansatz 的注意力网络来模拟量子电路,该神经网络负责自然语言处理领域的最新突破。我们通过模拟构建 Greenberger-Horne-Zeilinger 和多达 60 个量子位的线性图状态的电路,以及用于在多种系统尺寸上准备横向场 Ising 模型的基态的变分量子本征求解器电路来演示我们的方法。我们的方法构成了一种用于模拟量子物理的现代机器学习方法,适用于量子电路以及其他量子多体系统。我们通过模拟构建 Greenberger-Horne-Zeilinger 和多达 60 个量子位的线性图状态的电路,以及用于在多种系统尺寸上准备横向场 Ising 模型的基态的变分量子本征求解器电路来演示我们的方法。我们的方法构成了一种用于模拟量子物理的现代机器学习方法,适用于量子电路以及其他量子多体系统。我们通过模拟构建 Greenberger-Horne-Zeilinger 和多达 60 个量子位的线性图状态的电路,以及用于在多种系统尺寸上准备横向场 Ising 模型的基态的变分量子本征求解器电路来演示我们的方法。我们的方法构成了一种用于模拟量子物理的现代机器学习方法,适用于量子电路以及其他量子多体系统。
更新日期:2021-09-21
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