Scaling of neural-network quantum states for time evolution

• URL: http://arxiv.org/abs/2104.10696v1
• Date: Wed, 21 Apr 2021 18:00:07 GMT
• Title: Scaling of neural-network quantum states for time evolution
• Authors: Sheng-Hsuan Lin, Frank Pollmann
• Abstract summary: We benchmark the variational power of different shallow and deep neural autoregressive quantum states to simulate global dynamics of a non-integrable quantum Ising chain. We find that the number of parameters required to represent the quantum state at a given accuracy increases exponentially in time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating quantum many-body dynamics on classical computers is a challenging problem due to the exponential growth of the Hilbert space. Artificial neural networks have recently been introduced as a new tool to approximate quantum-many body states. We benchmark the variational power of different shallow and deep neural autoregressive quantum states to simulate global quench dynamics of a non-integrable quantum Ising chain. We find that the number of parameters required to represent the quantum state at a given accuracy increases exponentially in time. The growth rate is only slightly affected by the network architecture over a wide range of different design choices: shallow and deep networks, small and large filter sizes, dilated and normal convolutions, with and without shortcut connections.

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