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Status: Unchecked ×Keyword: parameter scaling ×Clear all

2 claims from 1 paper

1. Physics and Astronomy › Quantum many-body systems

   Scaling of Neural‐Network Quantum States for Time Evolution

   Lin and Pollmann · physica status solidi (b) · 2021

   The authors tested neural-network quantum states on global quench dynamics of a non-integrable quantum Ising chain, and found the parameters needed grow exponentially in time whatever the architecture.

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   1. UncheckedIn simulations of a quantum Ising chain, the number of neural-network parameters needed for a given accuracy grows exponentially with time.“We find that the number of parameters required to represent the quantum state at a given accuracy increases exponentially in time.”
   2. UncheckedIn these simulations, the growth rate of parameters needed over time changed only slightly across many neural-network design choices.“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.”