Bayesian sequential experiment design for quantum tomography
Seminar Room 1, Newton Institute
AbstractQuantum tomography is a valuable tool in quantum information processing and ex- perimental quantum physics, being essential for characterisation of quantum states, processes, and measurement equipment. Quantum state tomography (QST) aims to determine the unobservable quantum state of a system from outcomes of measurements performed on an ensemble of identically prepared systems. Measurements in quantum systems are non-deterministic, hence QST is a classical statistical estimation problem.
Full tomography of quantum states is inherently resource-intensive: even in moder- ately sized systems these experiments often take weeks. Sequential optimal experiment design aims at making these experiments shorter by adaptively reconfiguring the mea- surement in the light of partial data. In this talk, I am going to introduce the problem of quantum state tomography from a statistical estimation perspective, and describe a sequential Bayesian Experiment Design framework that we developed. I will report simulated experiments in which our framework achieves a ten-fold reduction in required experimentation time.
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