Entanglement-Based Machine Learning on a Quantum Computer

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.

6 pages, 4 figures, 2 tables, updated with the version published in PRL. This appears to be the first experimental paper in the field of quantum machine learning with growing interest

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• Quantum algorithm for solving linear systems of equations
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• A photonic cluster state machine gun
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• Demonstration of Shor's quantum factoring algorithm using photonic qubits
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