Generalizing Reinforcement Learning-Based Quantum Circuit Synthesis across Multiple Topologies

The optimization of the limited resources of current quantum devices hinges on the implementation of quantum-compiling techniques. The process of transforming high-level quantum algorithms into executable quantum circuits has a significant impact on the performance and fidelity of computations. One of the most critical aspects of the compiling stack is the synthesis of quantum circuits, which aims to minimize the depth and number of gates, as well as the error rate. Nevertheless, the optimal synthesis algorithms are extremely computationally expensive. This is why synthesis algorithms based on reinforcement learning (RL) have recently been introduced. However, such algorithms are constrained by the topology of quantum processors, necessitating retraining of the RL agent for different topologies. To achieve a balance between adaptability, optimality, and computational cost in RL-based quantum circuit synthesis techniques, this work proposes an RL-based quantum circuit synthesis algorithm of Clifford operators that addresses different topologies simultaneously. Experimental tests demonstrate that our synthesizer outperforms state-of-the-art greedy techniques while being significantly faster than boolean satisfiability solvers (SAT).