Adaptive Bayesian algorithm for achieving desired quantum transition

Abstract

Bayesian methods which utilize Bayes' theorem to update the knowledge of desired parameters after each measurement, are used in a wide range of quantum science. For various applications in quantum science, efficiently and accurately determining a quantum transition frequency is essential. However, the exact relation between a desired transition frequency and the controllable experimental parameters is usually absent. Here, we propose an efficient scheme to search the suitable conditions for a desired quantum transition via an adaptive Bayesian algorithm, and experimentally demonstrate it by using coherent population trapping in an ensemble of laser-cooled ${}^{87}$Rb atoms. The transition frequency is controlled by an external magnetic field, which can be tuned in realtime by applying a d.c. voltage. Through an adaptive Bayesian algorithm, the voltage can automatically converge to the desired one from a random initial value only after few iterations. In particular, when the relation between the target frequency and the applied voltage is nonlinear, our algorithm shows significant advantages over traditional methods. This work provides a simple and efficient way to determine a transition frequency, which can be widely applied in the fields of precision spectroscopy, such as atomic clocks, magnetometers, and nuclear magnetic resonance.

11 pages, 6 figures

References in corpus (21)

• A high-bias, low-variance introduction to Machine Learning for physicists
• Entanglement-free Heisenberg-limited phase estimation
• Experimental Quantum Hamiltonian Learning
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Robust Online Hamiltonian Learning
• Experimental Bayesian Quantum Phase Estimation on a Silicon Photonic Chip
• Machine learning meets quantum physics
• True precision limits in quantum metrology
• Experimental Phase Estimation Enhanced By Machine Learning
• A high-performance Raman-Ramsey Cs vapor cell atomic clock
• Experimental optical phase measurement approaching the exact Heisenberg limit
• Minimizing estimation runtime on noisy quantum computers
• Auto-Balanced Ramsey Spectroscopy
• Bayesian multi-parameter quantum metrology with limited data
• Entanglement-based dc magnetometry with separated ions
• Frequentist and Bayesian Quantum Phase Estimation
• Bayesian estimation for quantum sensing in the absence of single-shot detection
• Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
• Ramsey Spectroscopy with Displaced Frequency Jumps
• Adaptive tracking of a time-varying field with a quantum sensor
• Robustness of Quantum-Enhanced Adaptive Phase Estimation