Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selection as a sequential decision-making problem. CRiSP utilizes Neural-Guided Monte Carlo Tree Search, driven by a Transformer-based policy trained via s...
| Authors | Gino Kwun, Dhanvi Bharadwaj, Gokul Subramanian Ravi |
|---|---|
| Primary category | quant-ph |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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