可扩展性
计算机科学
量子电路
量子
蒙特卡罗方法
理论计算机科学
量子算法
算法
而量子蒙特卡罗
量子计算机
蒙特卡罗树搜索
树(集合论)
电子线路
计算机工程
量子纠错
数学
物理
数学分析
统计
量子力学
数据库
作者
Peiyong Wang,Muhammad Usman,Udaya Parampalli,Lloyd C. L. Hollenberg,Casey R. Myers
标识
DOI:10.1109/tqe.2023.3265709
摘要
Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years. Despite the adaptability and simplicity, their scalability and the selection of suitable ans\"atzs remain key challenges. In this work, we report an algorithmic framework based on nested Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed bandit (CMAB) model for the automated design of quantum circuits. Through numerical experiments, we demonstrated our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimisation on a graph, solving systems of linear equations, and finding encoding circuit for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimise quantum circuits for larger systems, showing both versatility and scalability.
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