Parameter estimation from quantum-jump data using neural networks
Journal article, 2024
We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings.
quantum metrology
photon counting
deep learning
neural networks
quantum parameter estimation
quantum jumps
Author
Enrico Rinaldi
RIKEN
Quantinuum K.K.
Manuel González Lastre
Universidad Autonoma de Madrid (UAM)
Instituto Universitario de Ciencia de Materiales Nicolás Cabrera
Sergio García Herreros
Universidad Autonoma de Madrid (UAM)
Instituto Universitario de Ciencia de Materiales Nicolás Cabrera
Shahnawaz Ahmed
Chalmers, Microtechnology and Nanoscience (MC2), Applied Quantum Physics
Maryam Khanahmadi
Chalmers, Microtechnology and Nanoscience (MC2), Applied Quantum Physics
F. Nori
University of Michigan
RIKEN
Carlos Sánchez Munõz
Instituto Universitario de Ciencia de Materiales Nicolás Cabrera
Universidad Autonoma de Madrid (UAM)
CSIC - Instituto de Fisica Fundamental (IFF)
Quantum Science and Technology
20589565 (eISSN)
Vol. 9 3 035018Subject Categories
Computer Engineering
DOI
10.1088/2058-9565/ad3c68