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Pré-Publication, Document De Travail Année : 2020

Finding Quantum Critical Points with Neural-Network Quantum States

Résumé

Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.
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Dates et versions

hal-02865833 , version 1 (12-06-2020)

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Remmy Zen, Long My, Ryan Tan, Frédéric Hébert, Mario Gattobigio, et al.. Finding Quantum Critical Points with Neural-Network Quantum States. 2020. ⟨hal-02865833⟩
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