Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo
Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo
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DOI：
10.1103/prxquantum.3.010324
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发表时间：
2021-08
期刊：
影响因子：
9.7
通讯作者：
E. Rinaldi;Xizhi Han;Mohammad Hassan;Yuan Feng;F. Nori;M. McGuigan;M. Hanada
中科院分区：
文献类型：
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作者：
E. Rinaldi;Xizhi Han;Mohammad Hassan;Yuan Feng;F. Nori;M. McGuigan;M. Hanada
矩阵量子力学在理论物理中扮演着各种重要的角色，例如量子黑洞的全息描述。了解量子黑洞和纠缠在全息结构中的作用，对于开发更好的量子算法(量子纠错码)和实现量子引力理论至关重要。量子计算和深度学习为我们提供了研究矩阵量子力学动力学的潜在有用的方法。在本文中，我们对量子计算和矩阵量子力学的深度学习方法进行了系统的综述，并将它们与格子蒙特卡罗模拟进行了比较。特别是，我们通过计算低能谱来测试每种方法的性能。
Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the development of better quantum algorithms (quantum error correction codes) and for the realization of a quantum theory of gravity. Quantum computing and deep learning offer us potentially useful approaches to study the dynamics of matrix quantum mechanics. In this paper we perform a systematic survey for quantum computing and deep learning approaches to matrix quantum mechanics, comparing them to Lattice Monte Carlo simulations. In particular, we test the performance of each method by calculating the low-energy spectrum.