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 ansätzs 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.

32 pages, 24 figures

References in corpus (7)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum algorithm for solving linear systems of equations
• A Quantum Approximate Optimization Algorithm
• Neural Predictor based Quantum Architecture Search
• Quantum Architecture Search with Meta-learning
• Quantum Architecture Search via Deep Reinforcement Learning
• Quantum circuit architecture search on a superconducting processor

Cited by in corpus (5)

• Quantum Architecture Search: A Survey
• Distributed quantum architecture search
• Symmetry-informed transferability of optimal parameters in the Quantum Approximate Optimization Algorithm
• Quantum circuit synthesis via a random combinatorial search
• Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning