AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis. While these circuits can be efficiently simulated and represented as symplectic matrices, standard synthesis methods-such as the Aaronson-Gottesman algorithm-often yield sub-optimal circuits with excessively high gate counts. In this work, we introduce AlphaClifford, a model-based Reinforcement Learning framework powered by Monte Carlo Tree Search, designed to efficiently synthesize Clifford circuits from the fundamental gate set composed of H, S, and CNOT. By modeling the state space through the algebraic properties of the...
| Authors | Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza et al. |
|---|---|
| Primary category | quant-ph |
| Published | 19 August 2026 |
| Last updated at source | 22 August 2026 |
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