QUBO Formulations for Training Machine Learning Models
UT-Battelle, LLC（UT-Battelle有限责任公司）；U.S. Department of Energy（美国能源部）；Oak Ridge Leadership Computing Facility（橡树岭领导计算设施）；DOE Office of Science（美国能源部科学办公室）；Oak Ridge National Laboratory（橡树岭国家实验室）；Auburn University（奥本大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers like the D-Wave 2000Q can approximately solve NP-hard optimization problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore's law era. In order to solve a problem on adiabatic quantum computers, it must be formulated as a QUBO problem, which is a challenging task in itself. In this paper, we formulate the training problems of three machine learning models---linear regression, support vector machine (SVM) and equal-sized k-means clustering---as QUBO problems so that they can be trained on adiabatic quantum computers efficiently. We also analyze the time and space complexities of our formulations and compare them to the state-of-the-art classical algorithms for training these machine learning models. We show that the time and space complexities of our formulations are better (in the case of SVM and equal-sized k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.
Optimization of the quantization of dense neural networks from an exact QUBO formulation
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Jaén, 23071 Jaén, Spain（数据科学与计算智能安达卢西亚研究机构（DaSCI）、杰嫩大学）
Performance Evaluation of Ising and QUBO Variable Encodings in Boltzmann Machine Learning
Graduate School of Information Sciences（信息科学研究生院）；Tohoku University（东北大学）；Technology Strategy Division（技术战略部门）；SCSK Corporation（SCSK公司）；Department of Physics（物理系）；Institute of Science（科学研究所）；Research and Education Institute for Semiconductors and Informatics（半导体与信息学研究教育研究所）；Kumamoto University（熊本大学）；Sigma-i Co.（Sigma-i公司）
Diversity-Aware Adaptive Collocation for Physics-Informed Neural Networks via Sparse QUBO Optimization and Hybrid Coresets
Research Center for Artificial Intelligence（人工智能研究中心）；Innopolis University（因诺波利斯大学）；Moscow Center for Advanced Studies（莫斯科先进研究中心）；Laboratory of Quantum Computing（量子计算实验室）；Center for Engineering Systems and Sciences（工程系统与科学中心）；Central University（中央大学）；The Center for Top-Level Educational Programs in AI（人工智能顶级教育项目中心）；National Medical Research Center of Oncology（肿瘤医学研究中心）
Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems
Graduate School of Science and Technology, Keio University（Keio大学科学技术研究生院）；Keio University（Keio大学）；Keio University Sustainable Quantum Artificial Intelligence Center (KSQAIC)（Keio大学可持续量子人工智能中心）；Department of Applied Physics and Physico-Informatics, Keio University（Keio大学应用物理与物理信息学系）；Human Biology-Microbiome-Quantum Research Center (WPI-Bio2Q)（人类生物学-微生物组-量子研究中心）
