Bulletin of the American Physical Society
APS March Meeting 2023
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session S62: Machine Learning for Quantum Matter IV
8:00 AM–10:48 AM,
Thursday, March 9, 2023
Room: Room 417
Sponsoring Unit: DCOMP
Chair: Shiming Lei, Rice University
Abstract: S62.00006 : Invited Talk: Cristian BonatoBayesian inference for quantum sensing and model learning*
9:24 AM–10:00 AM
Muhammad Junaid Arshad
Here we present our progress on the application of Bayesian inference for the characterization of quantum systems. In a first set of experiments, we apply sequential Bayesian estimation to improve the performance of a quantum sensor based on a single electronic spin. Our protocols, implemented on a fast micro-controller, adaptively choose optimal experimental settings in real-time to estimate decoherence timescales and static magnetic fields. We show that online adaptive approaches can provide a speed-up by a factor up to one order of magnitude compared to their non-adaptive counterparts, and discuss the expected impact on quantum sensing.
In a second set of experiments, we focus on automated reconstruction of the model for an unknown system of quantum emitters based on the arrival times of the emitted photons. We employ a Markov-chain Monte Carlo (MCMC) approach to infer the distribution of multiple parameters. We also introduce an algorithm to reconstruct a Lindblad master equation that describes the system, only setting an assumption on the number of energy levels involved. We benchmark our algorithms on a system of two resonant self-assembled InGaAs quantum dots in the cooperative emission regime.
*This work is funded by the Engineering and Physical Sciences Research Council (EP/S000550/1 and EP/V053779/1), the Leverhulme Trust (RPG-2019-388) and the European Commission (QuanTELCO, 862721).
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