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.
Cite
@article{arxiv.1409.7770,
title = {Entanglement-Based Machine Learning on a Quantum Computer},
author = {X. -D. Cai and D. Wu and Z. -E. Su and M. -C. Chen and X. -L. Wang and L. Li and N. -L. Liu and Chao-Yang Lu and Jian-Wei Pan},
journal= {arXiv preprint arXiv:1409.7770},
year = {2019}
}
Comments
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