Quantum Annealing for Mobility Studies: Generation of GO/NO-GO Maps via Quantum-Assisted Machine Learning

Abstract

The goal of this project was to explore and provide a proof-of-concept approach to solving ground vehicle mobility-related problems on emerging quantum computing (QC) machines, in particular as embodied in the D-Wave quantum annealer systems. We identified the problem of generating go/no-go-like maps as a suitable target problem, which can be mapped into a problem amenable to QC, taking into consideration current hardware limitations. The go/no-go problem is first cast as a machine learning problem and subsequently solved using quantum annealing, while relying on classical high-performance computing simulations for the generation of the required training set.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2018
Accession Number
AD1209160

Entities

People

  • A. Perdomo-ortiz
  • J. Realpe-gomez
  • M. Benedetti
  • Marques A. Wilson
  • P. Jayakumar
  • Radu Serban

Organizations

  • National Aeronautics and Space Administration

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Programming
  • Computers
  • Elastic Properties
  • Information Science
  • Machine Learning
  • Mechanics
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Quantum Algorithms
  • Quantum Computers
  • Quantum Computing
  • Software Design
  • Unsupervised Machine Learning

Readers

  • Distributed Systems and Data Platform Development
  • Operations Research
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Quantum Computing