The INI has a new website!

This is a legacy webpage. Please visit the new site to ensure you are seeing up to date information.

Skip to content



Bayesian sequential experiment design for quantum tomography

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

Seminar Room 1, Newton Institute


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.


The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.

Back to top ∧