Bayesian sequential experiment design for quantum tomography

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Bayesian sequential experiment design for quantum tomography's image

Description:	Huszar, F; Houlsby, NMT (Engineering) Monday 26 September 2011, 14:00-14:10


Created:	2011-10-04 09:12
Collection:	Design and Analysis of Experiments Cambridge Statistics Initiative 2011
Publisher:	Isaac Newton Institute
Copyright:	Huszar, F; Houlsby, NMT
Language:	eng (English)


Abstract:	Quantum 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.

Abstract:

Quantum 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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