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Entanglement-Based Quantum Machine Learning

zqyin 添加于 2014-9-30 15:15 | 1202 次阅读 | 0 个评论

作者
Cai X-D, Wu D, Su Z-E, Chen M-C, Wang X-L, Li L, Liu N-L, Lu C-Y, Pan J-W
摘要
Machine learning--a branch of artificial intelligence--can learn from previous experience to optimize performance, which is of broad interest to various fields including computer sciences, financial analysis, robotics, bioinformatics and computational biology. A major challenge for machine learning in the \"big data\" age is the rapidly growing data size that could become intractable for classical computers. Recently, quantum machine learning algorithms were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental demonstration of entanglement-based supervised machine learning tasks with a functionality similar to a spam filter--classifying 2-, 4- and 8-dimensional vectors to different clusters--on a photonic quantum computer. The results show that the manipulation and classification of high-dimensional vectors, the core mathematical routine in machine learning, can be efficiently done on quantum computers. The method can be scaled to larger number of qubits, and may provide a new route to ultrafast machine learning.
详细资料
- 关键词: quant-ph; cond-mat.other
- 文献种类: Manual Script
- 期卷页: 2014年
- 日期: 2014-9-27
- 发布方式: arXiv e-prints
- 备注:arXiv:1409.7770v1; 15 pages, 2 figures, 2 tables, 32 references. have been waiting for reviewers\' comments after submission for about 140 days
学科领域自然科学 » 物理学
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