Quantum filter reduction for measurement-feedback control via unsupervised manifold learning

Details Quantum filter reduction for measurement-feedback control via unsupervised manifold learning Quantum filter reduction for measurement-feedback control via unsupervised manifold learning

2009-05-08

arxiv.org/abs/0905.1139v1

New J. Phys. 11 (2009) 105043.

Physics

Quantum Physics

15 pages, 6 figures

Scientific paper

10.1088/1367-2630/11/10/105043

We derive simple models for the dynamics of a single atom coupled to a cavity field mode in the absorptive bistable parameter regime by projecting the time evolution of the state of the system onto a suitably chosen nonlinear low-dimensional manifold, which is found by use of local tangent space alignment. The output field from the cavity is detected with a homodyne detector allowing observation of quantum jumps of the system between states with different average numbers of photons in the cavity. We find that the models, which are significantly faster to integrate numerically than the full stochastic master equation, largely reproduce the dynamics of the system, and we demonstrate that they are sufficiently accurate to facilitate feedback control of the state of the system based on the predictions of the models alone.

Affiliated with

Also associated with

No associations

Rating

Quantum filter reduction for measurement-feedback control via unsupervised manifold learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Quantum filter reduction for measurement-feedback control via unsupervised manifold learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Quantum filter reduction for measurement-feedback control via unsupervised manifold learning will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-703159