Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
This addresses the challenge of efficiently translating quantum algorithms into executable gates for quantum computing, with incremental improvements over existing methods.
The paper tackled the problem of quantum unitary synthesis by using supervised learning to approximate minimum description length and combining it with stochastic beam search, achieving faster synthesis times and higher success rates for complex circuits.
Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search to identify near optimal gate sequences. Our method relies on a lightweight model with zero-shot generalization, substantially reducing training overhead compared to prior baselines. Across multiple benchmarks, we achieve faster wall-clock synthesis times while exceeding state-of-the-art methods in terms of success rate for complex circuits.