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Abstract

The effectiveness of measurement-based feedback control (MBFC) protocols is hindered by the presence of measurement noise, which impairs the ability to accurately infer the underlying dynamics of a quantum system from noisy continuous measurement records. To circumvent this limitation, a real-time stochastic state estimation approach is proposed in this work, that enables noise-free monitoring of the conditional dynamics, including the full density matrix of the quantum system, despite using noisy measurement data. This, in turn, enables the development of precise MBFC strategies that leads to effective control of quantum systems by essentially mitigating the constraints imposed by measurement noise, and has potential applications in various feedback quantum control scenarios. This approach is particularly important for machine learning-based control, where the AI controller can be trained with arbitrary conditional averages of observables, including the full density matrix, to quickly and accurately learn control strategies.