Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis

2025-07-22

AI Summary

Low synthesis efficiency for quantum state preparation remains a critical challenge in both NISQ and fault-tolerant eras. Method: We propose a reinforcement learning–based quantum circuit synthesis framework that discretizes the continuous quantum state space and integrates tabular Q-learning with sparse matrix representations, enabling arbitrary target state compilation over a universal gate set. A novel hybrid reward mechanism is introduced, combining physics-informed static rewards with dynamic penalties—specifically penalizing gate congestion and repeated state visits—to enhance search efficiency and circuit quality. Results: On graph state preparation tasks with up to 7 qubits, our method consistently generates circuits with minimal depth and optimal gate count, significantly outperforming existing heuristic approaches. Empirical evaluation confirms its efficiency, robustness, and near-optimality.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central challenge in both the NISQ era and future fault-tolerant quantum computing. The approach utilizes tabular Q-learning, based on action sequences, within a discretized quantum state space, to effectively manage the exponential growth of the space dimension. The framework introduces a hybrid reward mechanism, combining a static, domain-informed reward that guides the agent toward the target state with customizable dynamic penalties that discourage inefficient circuit structures such as gate congestion and redundant state revisits. By leveraging sparse matrix representations and state-space discretization, the method enables scalable navigation of high-dimensional environments while minimizing computational overhead. Benchmarking on graph-state preparation tasks for up to seven qubits, we demonstrate that the algorithm consistently discovers minimal-depth circuits with optimized gate counts. Moreover, extending the framework to a universal gate set for arbitrary quantum states, it still produces minimal depth circuits, highlighting the algorithm's robustness and adaptability. The results confirm that this RL-driven approach efficiently explores the complex quantum state space and synthesizes near-optimal quantum circuits, providing a resource-efficient foundation for quantum circuit optimization.

Problem

• Efficient synthesis of quantum circuits for target states
• Managing exponential growth of quantum state space
• Optimizing gate counts and circuit depth

Innovation

• Hybrid reward mechanism combining static and dynamic penalties
• Tabular Q-learning in discretized quantum state space
• Sparse matrix representations for scalable navigation