We investigate that no-knowledge measurement-based feedback control is utilized to obtain the estimation precision of the detection efficiency. For the feedback operators that concern us, no-knowledge measurement is the optimal way to estimate the detection efficiency. We show that the higher precision can be achieved for the lower or larger detection efficiency. It is found that no-knowledge feedback can be used to cancel decoherence. No-knowledge feedback with a high detection efficiency can perform well in estimating frequency and detection efficiency parameters simultaneously. And simultaneous estimation is better than independent estimation given by the same probes.
The no-knowledge quantum feedback was proposed by Szigeti et al., Phys. Rev. Lett. 113, 020407 (2014), as a measurement-based feedback protocol for decoherence suppression for an open quantum system. By continuously measuring environmental noises and feeding back controls on the system, the protocol can completely reverse the measurement backaction and therefore suppress the system's decoherence. However, the complete decoherence cancellation was shown only for the instantaneous feedback, which is impractical in real experiments. Therefore, in this work, we generalize the original work and inv…
In this paper, we consider stochastic master equations describing the evolutions of quantum systems interacting with electromagnetic fields undergoing continuous-time measurements. In particular, we study feedback control of quantum spin-1/2 systems in the case of unawareness of initial states and in presence of measurement imperfections. We prove that the fidelity between the actual quantum filter and its associated estimated filter converges to one under appropriate assumption on the feedback controller. This shows the asymptotic convergence of such filters. In addition, for spin-J systems, …
We consider the problem of frequency estimation for a single bosonic field evolving under a squeezing Hamiltonian and continuously monitored via homodyne detection. In particular, we exploit reinforcement learning techniques to devise feedback control strategies achieving increased estimation precision. We show that the feedback control determined by the neural network greatly surpasses in the long-time limit the performances of both the "no-control" strategy and the standard "open-loop control" strategy, which we considered as benchmarks. We indeed observe how the devised strategy is able to …
We address the interaction-time optimization for frequency estimation in a two-level system. The goal is to estimate with maximum precision a stochastic perturbation. Our approach is valid for any figure of merit used to define optimality, and is illustrated for the variance and entropy. For the entropy, we clarify the connection to maximum-likelihood estimation. We devise novel estimation protocols with and without feedback. They outperform common protocols given in the literature. We design a probabilistic self-consistent protocol as an optimal estimation without feedback. It can improve cur…
