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20192026
most citedMachine learning effective models for quantum systems

18 citations · 43 across the 9 of their papers we have counts for

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Showing cond-mat.str-elShow all

7 papers · 1 filter

cond-mat.str-el2026

Neural quantum states in condensed matter: advances, best practices, and prospects

Jonas B. Rigo, Björn J. Wurst, Rajah Nutakki +2

Neural quantum states provide flexible variational representations of quantum many-body wave functions by combining neural-network parametrizations with Monte Carlo sampling. In th…

cond-mat.str-el2025

Operator Lanczos Approach enabling Neural Quantum States as Real-Frequency Impurity Solvers

Jonas B. Rigo, Markus Schmitt

To understand the intricate exchange between electrons of different bands in strongly correlated materials, it is essential to treat multi-orbital models accurately. For this purpo…

cond-mat.str-el2024★ 2 cited

Unsupervised Learning of Effective Quantum Impurity Models

Jonas B. Rigo, Andrew K. Mitchell

Generalized quantum impurity models -- which feature a few localized and strongly-correlated degrees of freedom coupled to itinerant conduction electrons -- describe diverse physic…

cond-mat.str-el2022★ 5 cited

Linear response quantum transport through interacting multi-orbital nanostructures

Emma L. Minarelli, Jonas B. Rigo, Andrew K. Mitchell

Nanoelectronics devices, such as quantum dot systems or single-molecule transistors, consist of a quantum nanostructure coupled to a macroscopic external electronic circuit. Thermo…

cond-mat.str-el2022★ 2 cited

Two-channel charge-Kondo physics in graphene quantum dots

Emma L. Minarelli, Jonas B. Rigo, Andrew K. Mitchell

Nanoelectronic quantum dot devices exploiting the charge-Kondo paradigm have been established as versatile and accurate analog quantum simulators of fundamental quantum impurity mo…

cond-mat.str-el2021★ 16 cited

Automatic Differentiable Numerical Renormalization Group

Jonas B. Rigo, Andrew K. Mitchell

Machine learning techniques have recently gained prominence in physics, yielding a host of new results and insights. One key concept is that of backpropagation, which computes the…

cond-mat.str-el2019★ 18 cited

Machine learning effective models for quantum systems

Jonas B. Rigo, Andrew K. Mitchell

The construction of good effective models is an essential part of understanding and simulating complex systems in many areas of science. It is a particular challenge for correlated…