Accession Number:

AD1076914

Title:

Design and Application of Quantum Annealing Sampling Algorithms

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

Booz Allen Hamilton Inc.

Personal Author(s):

• Tomlin, Casey
• Dulny, J D
• Riolo, Joseph

Report Date:

2019-07-03

Pagination or Media Count:

18

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.

Descriptors:

• artificial neural networks
• machine learning
• algorithms
• data science
• air force
• heuristic methods
• data set
• digital data
• sampling
• information science
• optimization
• quantum bits
• annealing
• artificial intelligence software
• decomposition
• free energy
• ground state
• learning
• lessons learned
• linear programming

Subject Categories:

• Cybernetics
• Numerical Mathematics
• Computer Programming and Software

Distribution Statement:

[A, Approved For Public Release]