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Efficient optimization of deep neural quantum states
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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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.
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