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Status: Unchecked ×Keyword: sign structure ×Clear all

2 claims from 1 paper

1. Computer Science › Quantum Computing Algorithms and Architecture

   Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape

   Bukov, Schmitt and Dupont · SciPost Physics · 2021

   The paper tests neural-network wavefunctions on the frustrated J1-J2 Heisenberg model, and concludes that a rugged energy landscape, not network expressivity or sampling, mainly limits the result.

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   1. UncheckedThe authors conclude that a rugged energy landscape is the main obstacle to approximating this frustrated magnet's ground state and learning its sign structure.“Through a detailed analysis of the individual components of the algorithm, we conclude that the rugged nature of the energy landscape constitutes the major obstacle in finding a satisfactory approximation to the ground state wavefunction, and prevents learning the correct sign structure.”
   2. Unchecked“In particular, we show that in the present setup the neural network expressivity and Monte Carlo sampling are not primary limiting factors.”