Quantum Monte Carlo Tree Search with Fixed Confidence

2026-09-27

AI Summary

This study addresses the query complexity bottleneck of Monte Carlo Tree Search (MCTS) under fixed confidence. It proposes a Quantum MCTS (QMCTS) algorithm that integrates threshold elimination with quantum amplitude estimation to reduce the overhead of stochastic leaf evaluations. Furthermore, a novel quantum phase test is introduced, and instance-dependent theoretical lower bounds are established within the quantum oracle model. The authors rigorously derive an upper bound on the query complexity of QMCTS. Through numerical simulations and experiments conducted on real quantum hardware, the proposed algorithm is shown to achieve significant speedups over classical methods while demonstrating practical feasibility.

Abstract

Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo tree search (MCTS) in the fixed-confidence setting, where the goal is to identify a near-optimal move in a given game tree with high probability while minimizing query complexity. We first formulate MCTS under a quantum oracle model. We then develop a quantum MCTS algorithm (QMCTS) that combines threshold-based elimination on the search tree with quantum Monte Carlo estimation to reduce the cost of evaluating stochastic leaf values. We establish an instance-dependent lower bound on the query complexity and derive a corresponding upper bound for QMCTS. The lower-bound analysis introduces a new quantum phase-testing result that may also be useful beyond MCTS. We validate the theoretical results through simulation experiments and further demonstrate the feasibility of QMCTS on real quantum hardware.

Problem

• Quantum Computing
• Monte Carlo Tree Search
• Fixed Confidence
• Query Complexity
• Decision Making

Innovation

• Quantum Monte Carlo Tree Search
• Fixed Confidence
• Query Complexity
• Quantum Phase-Testing
• Quantum Oracle Model