Quantum Machine Learning

Abstract

Develop quantum algorithms for topological and geometric analysis of data that can supply exponential speed ups over classical methods, even in the absence of qRAM. Develop a set of quantum Basic Linear Algebra Subroutines (qBLAS): these will provide the work horses for constructing quantum machine learning algorithms that provide exponential speed up for classical devices when qRAM is available. Work with experimentalists to design and realize large-scale qRAM. Develop quantum machine learning techniques for the analysis of quantum data that are exponentially more efficient than semi-classical methods for data analysis and tomography. Develop a theory of universal deep quantum learning, and of deep quantum learning on tunable integrated photonic circuits.

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Document Details

Document Type
Technical Report
Publication Date
Jul 26, 2022
Accession Number
AD1189440

Entities

People

  • Seth Lloyd

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Computational Science
  • Computer Programming
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Differential Equations
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Kernel Functions
  • Machine Learning
  • Neural Networks
  • Photonic Integrated Circuits
  • Quantum Algorithms
  • Quantum Computing
  • Quantum Information
  • Quantum Information Science
  • Quantum Measurement
  • Quantum Mechanics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Calculus or Mathematical Analysis
  • Integrated Circuit Design and Technology.

Technology Areas

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