Entanglement-Based Machine Learning on a Quantum Computer
- 19 March 2015
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review Letters
- Vol. 114 (11), 110504
- https://doi.org/10.1103/physrevlett.114.110504
Abstract
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing “big data” could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.Keywords
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Funding Information
- National Natural Science Foundation of China (2011CB921300)
- Chinese Academy of Sciences (2011CB921300)
- National Fundamental Research (2011CB921300)
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