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arXiv · 1409.7770

Entanglement-Based Machine Learning on a Quantum Computer

X. -D. Cai·D. Wu·Z. -E. Su·M. -C. Chen·X. -L. Wang·L. Li·N. -L. Liu·Chao-Yang Lu·Jian-Wei Pan
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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] was proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of 2-, 4-, and 8-dimensional vectors to different clusters using a small-scale photonic quantum computer, which is 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 a larger number of qubits, and may provide a new route to accelerate machine learning.

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X. -D. Cai, D. Wu, Z. -E. Su, M. -C. Chen, X. -L. Wang, L. Li, N. -L. Liu, Chao-Yang Lu, Jian-Wei Pan. 2014-09-27. Entanglement-Based Machine Learning on a Quantum Computer. https://doi.org/10.1103/physrevlett.114.110504

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