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Computational Materials Science

Boltzmann machine modeling of layered MoS2 synthesis on a quantum annealer

Jeremy Liu, Ankith Mohan, Rajiv K. Kalia, Aiichiro Nakano, Ken‐ichi Nomura, Priya D. Vashishta, Ke-Thia Yao

Published Dec 4, 2019• 14 citations
Publisher
DOI
10.1016/j.commatsci.2019.109429

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This paper describes the components and configurations available in a new quantum-assisted machine learning (QAML) framework. QAML is an open source package that provides a new and flexible test-bed for algorithms to train and evaluate Boltzmann machines (BMs) using quantum annealers. Quantum annealing processors enable the training capabilities for both restricted (RBM) and general Boltzmann machines (BM). These methods rely on the fidelity of samples from those devices, which approximate the characteristic Boltzmann distribution of the BM models. The models and optimization functions are built on top of the PyTorch and D-Wave Ocean libraries. The goal of this paper is to introduce developers and researchers to a familiar, yet flexible, software development framework, to explore the use of quantum computing in machine learning applications. The framework is open-source and has been made publicly available.