Quantum system identification by Bayesian analysis of noisy data: Beyond Hamiltonian tomography

We consider how to characterize the dynamics of a quantum system from a restricted set of initial states and measurements using Bayesian analysis. Previous work has shown that Hamiltonian systems can be well estimated from analysis of noisy data. Here we show how to generalize this approach to systems with moderate dephasing in the eigenbasis of the Hamiltonian. We illustrate the process for a range of three-level quantum systems. The results suggest that the Bayesian estimation of the frequencies and dephasing rates is generally highly accurate and the main source of errors are errors in the reconstructed Hamiltonian basis.

[1]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[2]  L. Hollenberg,et al.  Subspace confinement: how good is your qubit? , 2007, quant-ph/0702123.

[3]  D. Goldfarb A family of variable-metric methods derived by variational means , 1970 .

[4]  Simon Devitt,et al.  Physics-based mathematical models for quantum devices via experimental system identification , 2008 .

[5]  Andrew D. Greentree,et al.  Identifying a two-state Hamiltonian in the presence of decoherence , 2006 .

[6]  SG Schirmer,et al.  Experimental Hamiltonian identification for controlled two-level systems , 2004 .

[7]  Andrew D. Greentree,et al.  Identifying an experimental two-state Hamiltonian to arbitrary accuracy (11 pages) , 2005 .

[8]  Koji Maruyama,et al.  Indirect Hamiltonian identification through a small gateway , 2009, 0903.0612.

[9]  P. Zoller,et al.  Complete Characterization of a Quantum Process: The Two-Bit Quantum Gate , 1996, quant-ph/9611013.

[10]  R. Fletcher,et al.  A New Approach to Variable Metric Algorithms , 1970, Comput. J..

[11]  D. Shanno Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[12]  Franco Nori,et al.  Coupling strength estimation for spin chains despite restricted access , 2008, 0810.2866.

[13]  Marvin H. J. Guber Bayesian Spectrum Analysis and Parameter Estimation , 1988 .

[14]  B. M. Fulk MATH , 1992 .

[15]  S. Schirmer,et al.  Two-qubit Hamiltonian tomography by Bayesian analysis of noisy data , 2009, 0902.3434.

[16]  C. G. Broyden The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations , 1970 .

[17]  Isaac L. Chuang,et al.  Prescription for experimental determination of the dynamics of a quantum black box , 1997 .