Entanglement-Based Machine Learning on a Quantum Computer

TL;DR

This paper demonstrates the first experimental use of a photonic quantum computer to classify high-dimensional data vectors, showcasing quantum machine learning's potential for speedup and scalability.

Contribution

It presents the first experimental entanglement-based classification of vectors on a quantum computer, advancing practical quantum machine learning techniques.

Findings

Successful classification of 2-, 4-, and 8-dimensional vectors

Demonstration of quantum manipulation of high-dimensional data

Potential scalability to larger quantum systems

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.