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MAQ: Machine Learning on Adiabatic Quantum Computers

Prasanna Date, Kathleen E. Hamilton, Robert M. Patton, Travis S. Humble, Thomas E. Potok

Published Jan 1, 2024• 0 citations
Publisher

Abstract

Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.
DOI
10.11578/dc.20240307.2

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A computational model of adiabatic evolutionary quantum system (or AEQS, pronounced "eeh-ks") was introduced in [Yamakami,2022] as a sort of quantum annealing and its underlying input-driven Hamiltonians are generated quantum-algorithmically by various forms of quantum automata families (including 1qqaf's). We study an efficient way to accomplish certain machine learning tasks by training these AEQSs quantumly. When AEQSs are controlled by 1qqaf's, it suffices in essence to find an optimal 1qqaf that approximately solves a target relational problem. For this purpose, we develop a basic idea of approximately utilizing well-known quantum algorithms for quantum counting, quantum amplitude estimation, and quantum approximation. We then provide a rough estimation of the efficiency of our quantum learning algorithms for AEQSs.
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