Optimizing Quantum Reversible Circuits Using Reinforcement Learning

In light of the rapid advancements in quantum computing, the synthesis of quantum circuits has drawn significant attention. Quantum circuits are required to be reversible owing to their inherent physical characteristics. However, synthesizing such circuits and optimizing their performance present substantial challenges. In this paper, we proposed to adopt reinforcement learning for synthesizing the reversible circuits involving NOT, CNOT, Toffoli, and other relevant gates and, in doing so, minimize the quantum computational costs of the resulting circuits. The experimental results demonstrated that the reversible circuits generated using the proposed method outperform the prior methods in both the gate counts and the total cost.