5 papers
Unlocking photodetection for quantum sensing with Bayesian likelihood-free methods and deep learning
Mateusz Molenda, Lewis A. Clark, Marcin PÅodzieÅ +1
To operate quantum sensors at their quantum limit in real time, it is crucial to identify efficient data inference tools for rapid parameter estimation. In photodetection, the key…
Tracking time-varying signals with quantum-enhanced atomic magnetometers
Julia Amoros-Binefa, Morgan W. Mitchell, Jan Kolodynski
Quantum entanglement, in the form of spin squeezing, is known to improve the sensitivity of atomic instruments to static or slowly-varying quantities. Sensing transient events pres…
Optimal and efficient inference tools for field tracking with precessing spins
Klaudia Dilcher, Piotr Bania, Diana Mendez-Avalos +3
Precise, real-time monitoring of magnetic field evolution is important in applications including magnetic navigation and searches for physics beyond the standard model. One main fi…
Noisy atomic magnetometry with Kalman filtering and measurement-based feedback
Julia Amoros-Binefa, Jan Kolodynski
Sensing a magnetic field with an atomic magnetometer operated in real time presents significant challenges, primarily due to sensor non-linearity, the presence of noise, and the ne…
Efficient inference of quantum system parameters by Approximate Bayesian Computation
Lewis A. Clark, Jan Kolodynski
The ability to efficiently infer system parameters is essential in any signal-processing task that requires fast operation. Dealing with quantum systems, a serious challenge arises…