Search for a Quantum Speedup

Abstract

Quantum annealing promises solutions to certain computational problems that are beyond nonquantum capability, and companies such as D-Wave Systems aim to develop such devices. Although practical quantum annealers (QAs) have shown success in many fields including optimization, machine learning and material science, they are yet to prove quantum speedup over existing classical algorithms. The aim of our work is to nd computational problems that demonstrate quantum speedup on a D-Wave QA. To this end, we developed an approach to search intelligently for computational problems on a D-Wave QA. Our work is important as it provides a structured method to test the quantum speedup of D-Wave QA, which is vital for future research in the direction of practical quantum annealing. The novelty of our work is to formulate a search task for computational problems and cast it as an unsupervised learning problem.

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Document Details

Document Type
Technical Report
Publication Date
Sep 26, 2019
Accession Number
AD1083447

Entities

Organizations

  • University of Calgary

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Annealing
  • Artificial Intelligence
  • Autonomous Agents
  • Computer Programming
  • Computer Programs
  • Dynamic Programming
  • Ground State
  • Learning
  • Machine Learning
  • Normal Distribution
  • Optimization
  • Physical Properties
  • Quantum Computers
  • Quantum Tunneling
  • Spin States
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Instructional Design and Training Evaluation.
  • Neural Network Machine Learning.
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.

Technology Areas

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