Automated Quantum Circuit Design With Nested Monte Carlo Tree Search

Abstract

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 ansatzes remain key challenges. In this work, we report an algorithmic framework based on nested Monte Carlo tree search coupled with the combinatorial multiarmed bandit model for the automated design of quantum circuits. Through numerical experiments, we demonstrate our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimization on a graph, solving systems of linear equations, and finding encoding circuits for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimize quantum circuits for larger systems, showing both versatility and scalability.

Keywords

• Artificial intelligence (AI)
• Monte Carlo tree search (MCTS)
• quantum machine learning
• quantum neural networks
• variational quantum circuits (VQCs)