Machine learning phase transitions with a quantum processor

A. V. Uvarov, A. S. Kardashin, J. D. Biamonte

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Machine learning has emerged as a promising approach to unveil properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods - such as Monte Carlo - which are known to experience an exponential slowdown when simulating certain quantum systems. To overcome this slowdown while still leveraging machine learning, we propose a variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter. Our classifier is directly fed labeled states recovered by the variational quantum eigensolver algorithm, thereby avoiding the data-reading slowdown experienced in many applications of quantum enhanced machine learning. We propose families of variational ansatz states that are inspired directly by tensor networks. This allows us to use tools from tensor network theory to explain properties of the phase diagrams the presented quantum algorithm recovers. Finally, we propose a

Original languageEnglish
Article number012415
JournalPhysical Review A
Issue number1
Publication statusPublished - Jul 2020


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