Speaker
Description
Sophisticated learning techniques, such as Bayesian inference, neural networks or reinforcement learning are becoming increasingly popular as powerful tools to characterise and optimise quantum systems.
Here, I will report on our theoretical and experimental work to speed up the characterisation of quantum systems by exploiting online adaptive measurements and Bayesian inference. We have developed a system, comprising a hard-realtime microcontroller, that implements real-time adaptation of measurement settings, based on previous measurement outcomes (through Sequential MonteCarlo, with <100 microseconds update time). Such a system always operate near maximum sensitivity, even in cases where the optimal settings depend on the unknown parameter to be estimated. We have used this system to characterise decoherence timescales for a single spin qubit associated to a nitrogen-vacancy centre in diamond, showing an improvement in speed of one order of magnitude. We have also benchmarked different adaptive heuristics based on different Bayesian and frequentist statistical estimation bounds. These results offer opportunities in the characterisation of large-scale quantum systems, and in quantum sensing, by considerably increasing the measurement bandwidth.
Time permitting, I will also discuss our on-going work on learning models for quantum emitters from photon arrival times, utilising a Markov-chain MonteCarlo approach.
