Tractable Infinite-Horizon Stochastic Model Predictive Control for Quantum Filtering via Eigenstate Reduction

• URL: http://arxiv.org/abs/2511.05916v1
• Date: Sat, 08 Nov 2025 08:25:48 GMT
• Title: Tractable Infinite-Horizon Stochastic Model Predictive Control for Quantum Filtering via Eigenstate Reduction
• Authors: Yunyan Lee, Ian R. Petersen, Daoyi Dong,
• Abstract summary: We propose a tractable Model Predictive Control framework for finite-dimensional quantum systems.<n>Online SMPC step requires only deterministic propagation of the filter and a terminal fidelity evaluation.<n>We establish equivalence and mean-square stability guarantees, and validate the approach on multi-level and Ising-type systems.
• Score: 8.368020865178844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model predictive control has shown potential to enhance the robustness of quantum control systems. propose a tractable Stochastic Model Predictive Control (SMPC) framework for finite-dimensional quantum systems under continuous-time measurement and quantum filtering. Using the almost-sure eigenstate reduction of quantum trajectories, we prove that the infinite-horizon stochastic objective collapses to a fidelity term that is computable in closed form from the one-step averaged state. Consequently, the online SMPC step requires only deterministic propagation of the filter and a terminal fidelity evaluation. An advantage of this method is that it eliminates per-horizon Monte Carlo scenario sampling and significantly reduces computational load while retaining the essential stochastic dynamics. We establish equivalence and mean-square stability guarantees, and validate the approach on multi-level and Ising-type systems, demonstrating favorable scalability compared to sampling-based SMPC.

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