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Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
Sara Giordano, Kornikar Sen, Miguel A. Martin-Delgado
https://arxiv.org/abs/2507.16641

Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
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 mecha…