# Entanglement-based machine learning on a quantum computer.

@article{Cai2015EntanglementbasedML, title={Entanglement-based machine learning on a quantum computer.}, author={X-D Cai and D. Wu and Zu-En Su and M.-C. Chen and X.-L. Wang and Li Li and N-L Liu and C.-Y. Lu and J.-W. Pan}, journal={Physical review letters}, year={2015}, volume={114 11}, pages={ 110504 } }

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…

## 130 Citations

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This experiment establishes a general and scalable framework for quantum machine learning, which is readily accessible on other physical platforms, by adopting a tensor-network-based machine learning algorithm with an entanglement-guided optimization that achieves an efficient representation of the quantum feature space using matrix product states.

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This work analyzes a distance-based classifier that is realised by a simple quantum interference circuit that can be implemented with small-scale setups available today.

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The purpose of this study is to select two similar quantum nearest neighbor algorithms and use a simple dataset to give insight into how they work, highlight their quantum nature, and compare their performances on IBM's quantum simulator.

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The main ideas, recent developments and progress are described in a broad spectrum of research investigating ML and AI in the quantum domain, investigating how results and techniques from one field can be used to solve the problems of the other.

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This work shows basic ideas of quantum machine learning, and presents several new methods that combine classical machine learning algorithms and quantum computing methods, and introduces neural networks approach to quantum tomography problem.

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