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The effectiveness of measurement-based feedback control (MBFC) protocols ishampered by the presence of measurement noise, which affects the ability toaccurately infer the underlying dynamics of a quantum system from noisycontinuous measurement records to determine an accurate control strategy. Tocircumvent such limitations, this work explores a real-time stochastic stateestimation approach that enables noise-free monitoring of the conditionaldynamics including the full density matrix of the quantum system using noisymeasurement records within a single quantum trajectory -- a method we name as`conditional state tomography'. This, in turn, enables the development ofprecise MBFC strategies that lead to effective control of quantum systems byessentially mitigating the constraints imposed by measurement noise and haspotential applications in various feedback quantum control scenarios. Thisapproach is particularly useful for reinforcement-learning (RL)-based control,where the RL-agent can be trained with arbitrary conditional averages ofobservables, and/or the full density matrix as input (observation), to quicklyand accurately learn control strategies.

No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography