Adaptive tracking of a time-varying field with a quantum sensor

Abstract

Sensors based on single spins can enable magnetic field detection with very high sensitivity and spatial resolution. Previous work has concentrated on sensing of a constant magnetic field or a periodic signal. Here, we instead investigate the problem of estimating a field with non-periodic variation described by a Wiener process. We propose and study, by numerical simulations, an adaptive tracking protocol based on Bayesian estimation. The tracking protocol updates the probability distribution for the magnetic field, based on measurement outcomes, and adapts the choice of sensing time and phase in real time. By taking the statistical properties of the signal into account, our protocol strongly reduces the required measurement time. This leads to a reduction of the error in the estimation of a time-varying signal by up to a factor 4 compared to protocols that do not take this information into account.

10 pages, 6 figures

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• Cross-sensor feedback stabilization of an emulated quantum spin gyroscope
• Real-time two-axis control of a spin qubit
• Continuous Real-Time Sensing with a Nitrogen Vacancy Center via Coherent Population Trapping
• Extending qubit coherence by adaptive quantum environment learning
• Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor
• On the capability of a class of quantum sensors
• Real-time adaptive estimation of decoherence timescales for a single qubit
• Physics-informed tracking of qubit fluctuations
• Passive and active suppression of transduced noise in silicon spin qubits
• Experimental quantum-enhanced response function estimation
• Applications of model-aware reinforcement learning in Bayesian quantum metrology
• Model-aware reinforcement learning for high-performance Bayesian experimental design in quantum metrology
• Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories
• Time-adaptive phase estimation
• Real-Time Magnetometry Using Dark States of a Nitrogen Vacancy Center
• Adaptive Bayesian algorithm for achieving desired quantum transition