Details

Autor(en) / Beteiligte

• Cai, X-D
• Wu, D
• Su, Z-E
• Chen, M-C
• Wang, X-L
• Li, Li
• Liu, N-L
• Lu, C-Y
• Pan, J-W

Titel

Entanglement-based machine learning on a quantum computer

Ist Teil von

• Physical review letters, 2015-03, Vol.114 (11), p.110504-110504, Article 110504

Ort / Verlag

United States

Erscheinungsjahr

2015

Quelle

American Physical Society

Beschreibungen/Notizen

• 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 speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are 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 larger numbers of qubits, and may provide a new route to accelerate machine learning.