A quantum machine learning algorithm based on generative models

• Published in: Science advances Vol. 4; no. 12; p. eaat9004
• Main Authors: Gao, X., Zhang, Z.-Y., Duan, L.-M.
• Format: Journal Article
• Language: English
• Published: United States American Association for the Advancement of Science 07.12.2018
• Subjects: Physics; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.aat9004

We propose a quantum learning algorithm for a quantum generative model and prove its advantages compared with classical models. Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. A significant school of thought regarding artificial intelligence is based on generative models. Here, we propose a general quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically. Our result opens a new direction for quantum machine learning and offers a remarkable example where a quantum algorithm shows exponential improvement over classical algorithms in an important application field.