Dimension Reduction for Open Quantum Systems

Abstract

The overall aim of this project has been to develop and to demonstrate new tools for dimensional reduction of models for circuits/networks of coherently-coupled quantum dynamical systems. Such circuits/networks can be modeled straightforwardly in the limit of vanishing propagation time between components, using quantum stochastic differential equations. The overall dimension of the resulting models increases exponentially with component count, however, and prior to this project no optimized methods for incorporating time delays were known. The specific aims of this project are: 1) to apply manifold projection-based methods to develop component wise dimensional reduction methods for quantum systems, 2) to apply machine-learning methods to develop component-wise dimensional reduction methods for quantum systems, 3) to explore strategies based on quantum measurement theory and quantum stochastic differential equations to develop dimension-reducing approximations in the way that we treat coherent coupling between components, 4) to develop and to demonstrate general-purpose methods for incorporating time delays in quantum circuit/network models, and 5) to demonstrate the utility of quantum dimensional reduction methods by simulating systems of interest for distributed quantum information processing.

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Document Details

Document Type
Technical Report
Publication Date
Jul 07, 2023
Accession Number
AD1224650

Entities

People

  • Hideo Mabuchi

Organizations

  • Stanford University

Tags

Fields of Study

  • Physics

Readers

  • Calculus or Mathematical Analysis
  • Distributed Systems and Data Platform Development
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

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