Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
Quantum feedback control requires acting on noisy continuous measurement records without direct access to the underlying quantum state. We propose Kraus-Parameterized Belief Reinforcement Learning, a pipeline in which a recurrent encoder, constrained to the Stiefel manifold, produces density-matrix estimates that are guaranteed positive-semidefinite and trace-normalized by construction, embedding quantum state geometry directly into the learning loop. A Proximal Policy Optimization (PPO) actor then maps these physically valid belief states to continuous control actions. On a simulated continuously monitored qubit, the resulting policy achieves stable feedback control, maintaining a measur...
| Authors | Priyanshi Singh, Krishna Bhatia |
|---|---|
| Primary category | quant-ph |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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