| Autor: |
Pilati S; School of Science and Technology, Physics Division, Università di Camerino, 62032 Camerino (MC), Italy., Inack EM; Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada N2L 2Y5., Pieri P; School of Science and Technology, Physics Division, Università di Camerino, 62032 Camerino (MC), Italy and INFN, Sezione di Perugia, 06123 Perugia (PG), Italy. |
| Jazyk: |
angličtina |
| Zdroj: |
Physical review. E [Phys Rev E] 2019 Oct; Vol. 100 (4-1), pp. 043301. |
| DOI: |
10.1103/PhysRevE.100.043301 |
| Abstrakt: |
The projective quantum Monte Carlo (PQMC) algorithms are among the most powerful computational techniques to simulate the ground-state properties of quantum many-body systems. However, they are efficient only if a sufficiently accurate trial wave function is used to guide the simulation. In the standard approach, this guiding wave function is obtained in a separate simulation that performs a variational minimization. Here we show how to perform PQMC simulations guided by an adaptive wave function based on a restricted Boltzmann machine. This adaptive wave function is optimized along the PQMC simulation via unsupervised machine learning, avoiding the need of a separate variational optimization. As a byproduct, this technique provides an accurate ansatz for the ground-state wave function, which is obtained by minimizing the Kullback-Leibler divergence with respect to the PQMC samples, rather than by minimizing the energy expectation value as in standard variational optimizations. The high accuracy of this self-learning PQMC technique is demonstrated for a paradigmatic sign-problem-free model, namely, the ferromagnetic quantum Ising chain, showing very precise agreement with the predictions of the Jordan-Wigner theory and of loop quantum Monte Carlo simulations performed in the low-temperature limit. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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