Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911827743473664 |
|---|---|
| author | de Schoulepnikoff, Paulin Kiss, Oriel Vallecorsa, Sofia Carleo, Giuseppe Grossi, Michele |
| author_facet | de Schoulepnikoff, Paulin Kiss, Oriel Vallecorsa, Sofia Carleo, Giuseppe Grossi, Michele |
| contents | Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this challenge, we propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum. Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging. Moreover, by controlling the amount of required resources, this scheme can be applied to larger, as well as non permutation invariant systems, where the latter constraint is associated with the Heisenberg forging procedure. We substantiate our findings through numerical simulations conducted on spins models exhibiting one-dimensional ring, two-dimensional triangular lattice topologies, and nuclear shell model configurations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_02633 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging de Schoulepnikoff, Paulin Kiss, Oriel Vallecorsa, Sofia Carleo, Giuseppe Grossi, Michele Quantum Physics Statistical Mechanics Machine Learning Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this challenge, we propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum. Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging. Moreover, by controlling the amount of required resources, this scheme can be applied to larger, as well as non permutation invariant systems, where the latter constraint is associated with the Heisenberg forging procedure. We substantiate our findings through numerical simulations conducted on spins models exhibiting one-dimensional ring, two-dimensional triangular lattice topologies, and nuclear shell model configurations. |
| title | Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging |
| topic | Quantum Physics Statistical Mechanics Machine Learning |
| url | https://arxiv.org/abs/2307.02633 |