Design and Application of Quantum Annealing Sampling Algorithms
Abstract
The objective of this effort was to investigate the utility of hardware quantum annealing devices in two near-term applications: Circuit fault diagnosis; and machine learning. This report summarizes the technical work performed, results published, software developed, lessons learned, and future directions of study. Key accomplishments include: A new efficient algorithm for domain decomposition, allowing large optimization and sampling problems to be solved on small quantum hardware; A library of optimal Hamiltonians for common circuits; Software for rapid experimentation in quantum-assisted unsupervised Boltzmann machine training; and Training of several quantum hardware-native and non-native Boltzmann machines with state-of-the-art performance on standard benchmarks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 03, 2019
- Accession Number
- AD1076914
Entities
People
- Casey Tomlin
- J D Dulny
- Joseph Riolo
Organizations
- Booz Allen Hamilton