Automatic differentiable numerical renormalization group
نویسندگان
چکیده
Machine learning techniques have recently gained prominence in physics, yielding a host of new results and insights. One key concept is that backpropagation, which computes the exact gradient any output program with respect to input. This achieved efficiently within differentiable programming paradigm, utilizes automatic differentiation (AD) each step computer chain rule. A classic application training neural networks. Here, we apply this methodology instead numerical renormalization group (NRG), powerful technique computational quantum many-body physics. We demonstrate how derivatives NRG outputs Hamiltonian parameters can be accurately obtained. Physical properties calculated using scheme---for example, thermodynamic observables from free energy. Susceptibilities computed by adding source terms Hamiltonian, but still evaluated AD at precisely zero field. As an outlook, briefly discuss dynamical quantities possible route vertex.
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ژورنال
عنوان ژورنال: Physical review research
سال: 2022
ISSN: ['2643-1564']
DOI: https://doi.org/10.1103/physrevresearch.4.013227