Quantum machine learning with D‐wave quantum computer

Quantum EngineeringVolume 1, Issue 2 e12 COMPREHENSIVE REVIEW Quantum machine learning with D-wave quantum computer Feng Hu, Feng Hu orcid.org/0000-0002-3981-3979 Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorBan-Nan Wang, Ban-Nan Wang Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorNing Wang, Ning Wang Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorChao Wang, Corresponding Author Chao Wang wangchao@shu.edu.cn Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China Chao Wang, Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China. Email: wangchao@shu.edu.cnSearch for more papers by this author Feng Hu, Feng Hu orcid.org/0000-0002-3981-3979 Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorBan-Nan Wang, Ban-Nan Wang Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorNing Wang, Ning Wang Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, ChinaSearch for more papers by this authorChao Wang, Corresponding Author Chao Wang wangchao@shu.edu.cn Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China Chao Wang, Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China. Email: wangchao@shu.edu.cnSearch for more papers by this author First published: 08 May 2019 https://doi.org/10.1002/que2.12Citations: 22Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Summary The new era of artificial intelligence (AI) aims to entangle the relationships among models (characterizations), algorithms, and implementations toward the high-level intelligence with general cognitive ability, strong robustness, and interpretability, which is intractable for machine learning (ML). Quantum computer provides a new computing paradigm for ML. Although universal quantum computers are still in infancy, special-purpose D-Wave machine hopefully becomes the breaking point of commercialized quantum computing. The core principle, quantum annealing (QA), enables the quantum system to naturally evolve toward the low-energy states. D-Wave's quantum computer has developed some applications of quantum ML based on quantum-assisted ML algorithms, quantum Boltzmann machine, etc. Additionally, working with CPUs, quantum processing units is likely to advance ML in a quantum-inspired way. Thus, a new advanced computing architecture, quantum-classical hybrid approach consisting of QA, classical computing, and brain-inspired cognitive science, is required to explore its superiority to universal quantum algorithms and classical ML algorithms. It is important to explore hybrid quantum/classical approaches to overcome the defects of ML such as high dependence on training data, low robustness to the noises, and cognitive impairment. The new framework is expected to gradually form a highly effective, accurate, and adaptive intelligent computing architecture for the next generation of AI. Citing Literature Volume1, Issue2June 2019e12 RelatedInformation