Quantum Circuit Pre-Synthesis: Learning Local Edits to Reduce $T$-count
This work addresses the challenge of high T-count in Clifford+T circuits, which significantly increases resource overhead in fault-tolerant quantum computing. Existing local synthesis methods are constrained by circuit representations and struggle to achieve optimal T-count and depth. To overcome this limitation, the paper introduces Q-PreSyn, the first approach to integrate reinforcement learning into the pre-synthesis phase of quantum circuit compilation. By training an agent to learn sequences of function-preserving local editing operations, Q-PreSyn produces circuit representations that are more amenable to downstream synthesis. Without introducing any approximation error, the method achieves up to a 20% reduction in T-count compared to state-of-the-art techniques on circuits with up to 25 qubits, substantially improving synthesis efficiency.