QFlowNet: Fast, Diverse, and Efficient Unitary Synthesis with Generative Flow Networks
Unitary Synthesis, the decomposition of a unitary matrix into a sequence of quantum gates, is a fundamental challenge in quantum compilation. Prevailing reinforcement learning (RL) approaches are often hampered by sparse reward signals, which necessitate complex reward shaping or long training times, and typically converge to a single policy, lacking solution diversity. In this work, we propose QFlowNet, a novel framework that learns efficiently from sparse signals by pairing a Generative Flow Network (GFlowNet) with Transformers. Our approach addresses two key challenges. First, the GFlowNet framework is fundamentally designed to learn a diverse policy that samples solutions proportional...
| Authors | Inhoe Koo, Hyunho Cha, Jungwoo Lee |
|---|---|
| Primary category | quant-ph |
| Published | 3 March 2026 |
| Last updated at source | 4 March 2026 |
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