31 citations · 88 across the 9 of their papers we have counts for
10 papers
Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models
Vinicius Hernandes, Joseph Rogers, Rouven Koch +5
Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability dia…
Hamiltonian learning quantum magnets with dynamical impurity tomography
Netta Karjalainen, Greta Lupi, Rouven Koch +2
Nanoscale engineered spin systems, ranging from spins on surfaces to nanographenes, provide flexible platforms to realize entangled quantum magnets from a bottom-up approach. Howev…
Hamiltonian Learning of Triplon Excitations in an Artificial Nanoscale Molecular Quantum Magnet
Rouven Koch, Robert Drost, Peter Liljeroth +1
Extracting the Hamiltonian parameters of nanoscale quantum magnets from experimental measurements is a significant challenge in quantum matter. Here we establish a machine learning…
Cross-Platform Autonomous Control of Minimal Kitaev Chains
David van Driel, Rouven Koch, Vincent P. M. Sietses +21
Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity…
Hamiltonian learning with real-space impurity tomography in topological moire superconductors
Maryam Khosravian, Rouven Koch, Jose L. Lado
Extracting Hamiltonian parameters from available experimental data is a challenge in quantum materials. In particular, real-space spectroscopy methods such as scanning tunneling sp…
Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
Rouven Koch, David van Driel, Alberto Bordin +2
Determining Hamiltonian parameters from noisy experimental measurements is a key task for the control of experimental quantum systems. An experimental platform that recently emerge…
Hamiltonian inference from dynamical excitations in confined quantum magnets
Netta Karjalainen, Zina Lippo, Guangze Chen +3
Quantum-disordered models provide a versatile platform to explore the emergence of quantum excitations in many-body systems. The engineering of spin models at the atomic scale with…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Designing quantum many-body matter with conditional generative adversarial networks
Rouven Koch, Jose L. Lado
The computation of dynamical correlators of quantum many-body systems represents an open critical challenge in condensed matter physics. While powerful methodologies have risen in…
Neural network enhanced hybrid quantum many-body dynamical distributions
Rouven Koch, Jose L. Lado
Computing dynamical distributions in quantum many-body systems represents one of the paradigmatic open problems in theoretical condensed matter physics. Despite the existence of di…