Dimension reduction for open quantum systems

Abstract

Major goals of this research project include the following: 1. Machine learning of input-output maps for open quantum systems 2. Structure-preserving input-output model reduction for open quantum systems 3. Exploration of direct methods for dimensional reduction of QSDE models 4. Practical demonstration of hierarchical quantum input-output model reduction These technical objectives are to be pursued in the context of applications in quantum engineering, quantum metrology and quantum information science.

Document Details

Document Type
DoD Grant Award
Publication Date
Dec 04, 2018
Source ID
W911NF1610086

Entities

People

  • Hideo Mabuchi

Organizations

  • Army Contracting Command
  • Stanford University
  • United States Army

Tags

Fields of Study

  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing