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. 2015 Mar 20;114(11):110504.
doi: 10.1103/PhysRevLett.114.110504. Epub 2015 Mar 19.

Entanglement-based machine learning on a quantum computer

X-D Cai  1   2 , D Wu  1   2 , Z-E Su  1   2 , M-C Chen  1   2 , X-L Wang  1   2 , Li Li  1   2 , N-L Liu  1   2 , C-Y Lu  1   2 , J-W Pan  1   2
Affiliations

Entanglement-based machine learning on a quantum computer

X-D Cai et al. Phys Rev Lett. .

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.

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