Quantum Machine Learning
Abstract
Quantum Machine Learning: Abstract Research Objective: The goal of this proposal is to develop quantum machine learning techniques that can recognize and classify patterns that are hard to recognize classically. We will develop quantum machine learning algorithms that are exponentially faster than their classical counterparts, and will work with experimentalists to realize small quantum computers and special-purpose quantum information processors that can implement those algorithms. Technical approach: We will develop quantum machine algorithms for small universal quantum computers consisting of 50- 1000 qubits and capable of performing O(10^4) coherent logic operations prior to error correction. Supplemented by quantum random access memory (qRAM) such small quantum computers can perform a wide variety of machine learning algorithms, such as cluster assignment or principal component analysis, exponentially faster than classical computers. Even without qRAM, such devices can perform topological and geometrical analysis of data exponentially faster than classical computers. In addition, small quantum computers can be used to perform machine learning on quantum data, the quantum states output by quantum sensors and detectors. We will develop machine learning techniques for deep quantum learning performed by special-purpose, non-universal devices such as quantum annealers and large-scale tunable in-tegrated optical circuits. Finally, we will develop methods for universal deep quantum learning, designing and training quantum circuits capable of universal quantum computation to perform machine learning and pattern recognition. Anticipated outcome: Quantum machine learning techniques on special-purpose and universal quantum information processors will provide powerful enhancements for the analysis of classical and quantum data.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 11, 2018
- Source ID
- W911NF1710527
Entities
People
- Seth Lloyd
Organizations
- Army Contracting Command
- Massachusetts Institute of Technology
- Office of the Secretary of Defense