Abstract: Quantum sensing holds great promise for high-precision magnetic field measurements. However, its performance is significantly limited by noise. The investigation of active quantum error correction to address this noise led to the Hamiltonian-not-in-Lindblad-span (HNLS) condition. This states that the Heisenberg scaling is achievable if and only if the signal Hamiltonian is orthogonal to the […]
In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]
In this article, we propose a machine-learning framework for parameter estimation of single-mode Gaussian quantum states. Under a Bayesian framework, our approach estimates parameters of suitable prior distributions from measured data. For phase-space displacement and squeezing parameter estimation, this is achieved by introducing expectation–maximization (EM)-based algorithms, while for phase parameter estimation, an empirical Bayes method […]