Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
This work addresses scalability issues in quantum computing for researchers and practitioners, but it is incremental as it builds on existing MCTS and bandit methods.
The paper tackled the challenge of scalability and ansatz selection in variational quantum algorithms by developing an automated quantum circuit design framework using nested Monte Carlo Tree Search with combinatorial multi-armed bandit models, demonstrating its application to problems like quantum chemistry and optimization with improved exploration of larger search spaces and scalability for larger systems.
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.