Efficient optimization of deep neural quantum states

IF 17.6 1区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

Nature Physics Pub Date : 2024-07-09 DOI:10.1038/s41567-024-02567-0

引用次数: 0

Abstract

An improved optimization algorithm enables the training of large-scale neural quantum states in which the enormous number of neuron connections capture the intricate complexity of quantum many-body wavefunctions. This advance leads to unprecedented accuracy in paradigmatic quantum models, opening up new avenues for simulating and understanding complex quantum phenomena.

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深度神经量子态的高效优化

改进后的优化算法能够训练大规模神经量子态，其中大量神经元连接捕捉了量子多体波函数的复杂性。这一进步使典型量子模型达到了前所未有的精确度，为模拟和理解复杂量子现象开辟了新途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Physics 物理-物理：综合

CiteScore

30.40

自引率

2.00%

发文量

349

审稿时长

4-8 weeks

期刊介绍： Nature Physics is dedicated to publishing top-tier original research in physics with a fair and rigorous review process. It provides high visibility and access to a broad readership, maintaining high standards in copy editing and production, ensuring rapid publication, and maintaining independence from academic societies and other vested interests. The journal presents two main research paper formats: Letters and Articles. Alongside primary research, Nature Physics serves as a central source for valuable information within the physics community through Review Articles, News & Views, Research Highlights covering crucial developments across the physics literature, Commentaries, Book Reviews, and Correspondence.