Machine Learning Phase Transitions with a Quantum Processor

24 Jun 2019Alexey UvarovAndrey KardashinJacob Biamonte

Machine learning has emerged as a promising approach to study the 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... (read more)

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