Quantum System Identification via L1-norm Minimization

Abstract

This report summarizes our efforts to apply the theory and algorithms of Compressed Sensing (CS) to Quantum Process Tomography (QPT) and Hamiltonian parameter estimation. Specific results include: (1) Development of computational algorithms to include physics based constraints on the quantum process matrix, i.e., positive-semidefinite and trace preserving. (2) Simulations of two-qubit Quantum Fourier Transform interacting with an unknown environment. (3) Establishment of robustness of ideal unitary basis via singular-value-decomposition. (4) The first experimental demonstration of QPT via CS on a photonic system at the University of Queensland. The latter experimental results showed the anticipated and predicted significant reduction of estimation resources, e.g., with respect to an estimate of a 16x16 process matrix obtained from an over complete set of 576 configurations, only 32 configurations were needed to obtain a 97% fidelity, and only 18 configurations to obtain a 94% fidelity. (5) Application of CS to a nearly-sparse many-body Hamiltonian.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 2011
Accession Number
ADA566222

Entities

People

  • Hersch Rabitz
  • Robert L. Kosut

Tags

Communities of Interest

  • Advanced Electronics
  • Biomedical
  • C4I

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Coding
  • Compressed Sensing
  • Computational Science
  • Estimators
  • Exclusion Principle
  • Information Processing
  • Interferometers
  • Ion Traps
  • Measurement
  • Quantum Computing
  • Quantum Information
  • Quantum Mechanics
  • Quantum Properties
  • Signal Processing
  • Waveplates

Fields of Study

  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

  • Quantum Computing