Online Parameter Estimation for Continuously Monitored Quantum Systems

Abstract

In this work, we consider the problem of online (real-time, single-shot) estimation of static or slow-varying parameters along quantum trajectories in quantum dynamical systems. Based on the measurement signal of a continuously-monitored quantum system, we propose a recursive algorithm for computing the maximum likelihood estimate of unknown parameters using an approach based on stochastic gradient ascent on the log-likelihood function. We formulate the algorithm in both discrete-time and continuous-time and illustrate the performance of the algorithm through simulations of a simple two-level system undergoing homodyne measurement from which we are able to track multiple parameters simultaneously.

References in corpus (6)

• Circuit Quantum Electrodynamics
• Quantum Kalman Filtering and the Heisenberg Limit in Atomic Magnetometry
• Robust quantum parameter estimation: coherent magnetometry with feedback
• Bayesian parameter inference from continuously monitored quantum systems
• Single shot parameter estimation via continuous quantum measurement
• Multi-parameter estimation along quantum trajectories with Sequential Monte Carlo methods

Cited by in corpus (1)

• Distributed multi-parameter quantum metrology with a superconducting quantum network