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arXiv:2412.07666 · Physical Review Applied

Hamiltonian-learning quantum magnets with nonlocal impurity tomography

Greta Lupi, José Luis Lado

Published May 27, 2025• 5 citations
arXivHTMLPDF

Abstract

Impurities in quantum materials have provided successful strategies for learning properties of complex states, ranging from unconventional superconductors to topological insulators. In quantum magnetism, inferring the Hamiltonian of an engineered system becomes a challenging open problem in the presence of complex interactions. Here we show how a supervised machine-learning technique can be used to infer Hamiltonian parameters from atomically engineered quantum magnets by inferring fluctuations of the ground states due to the presence of impurities. We demonstrate our methodology both with a fermionic model with spin-orbit coupling, as well as with many-body spin models with long-range exchange and anisotropic exchange interactions. We show that our approach enables performing Hamiltonian extraction in the presence of significant noise, providing a strategy to perform Hamiltonian learning with experimental observables in atomic-scale quantum magnets. Our results establish a strategy to perform Hamiltonian learning by exploiting the impact of impurities in complex quantum many-body states.

DOI
10.1103/physrevapplied.23.054077

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Impurities in quantum materials have provided successful strategies for learning properties of complex states, ranging from unconventional superconductors to topological insulators. In quantum magnetism, inferring the Hamiltonian of an engineered system becomes a challenging open problem in the presence of complex interactions. Here we show how a supervised machine-learning technique can be used to infer Hamiltonian parameters from atomically engineered quantum magnets by inferring fluctuations of the ground states due to the presence of impurities. We demonstrate our methodology both with a fermionic model with spin-orbit coupling, as well as with many-body spin models with long-range exchange and anisotropic exchange interactions. We show that our approach enables performing Hamiltonian extraction in the presence of significant noise, providing a strategy to perform Hamiltonian learning with experimental observables in atomic-scale quantum magnets. Our results establish a strategy to perform Hamiltonian learning by exploiting the impact of impurities in complex quantum many-body states.
Nanoscale engineered spin systems, ranging from spins on surfaces to nanographenes, provide flexible platforms to realize entangled quantum magnets from a bottom-up approach. However, assessing the quantum many-body Hamiltonian realized in a specific experiment remains an exceptional open challenge, due to the difficulty of disentangling competing terms accounting for the many-body excitations. Here, we demonstrate a machine learning strategy to learn a quantum many-body spin Hamiltonian from scanning spectroscopy measurements of spin excitations. Our methodology leverages the spatially resolved reconstruction of the many-body excitations induced by depositing quantum impurities next to the quantum magnet. We demonstrate that our algorithm allows us to predict long-range Heisenberg exchange interactions, anisotropic exchange, and antisymmetric Dzyaloshinskii-Moriya interaction, including in the presence of sizable noise. Our methodology establishes defect-induced spatially resolved dynamical excitations in quantum magnets as a powerful strategy to understand the nature of quantum spin many-body models.
One-dimensional quantum spin chains provide a versatile platform for design of artificial quantum matter that enables breakthroughs in spintronics and quantum computing. These spin systems can be engineered at the atomic scale with scanning tunneling microscopy and their local excitations can be measured with electrically driven spin resonance. When engineering these system, a major open challenge is to extract parameters of the many-body spin Hamiltonian, since the extraction must be done indirectly. This thesis presents a machine learning technique that allows to infer the underlying Hamiltonian parameters via dynamical spin excitations. The methodology is based on the exploitation of many-body reconstructions around engineered quantum impurities adjacent to S=1/2 spin chains. These systems can be realized by hydrogenated titanium atoms in magnesium oxide films, featuring S=1/2 impurities next to the chain. The presented supervised methodology exploits the local dynamical correlator functions to provide the full many-body Hamiltonian as a result. Two neural networks using different quantum impurity placements are implemented. The networks allow to predict long range Heisenberg exchange interactions J2 and J3, anisotropic exchange interaction JZ, as well as antisymmetric Dzyaloshinskii-Moriya exchange interaction JDMI accurately for spin chain with a length of 21 spins from computational data. It is demonstrated that the approach is robust to noise in the spin excitations, offering an experimentally realistic strategy for Hamiltonian learning. It is also shown that different impurity placements offer better estimates for the parameters. With further research, the networks could be used to predict the parameters from experimental data. The introduced technique demonstrates the applicability of machine learning methodology to study nano-scale quantum systems and puts forward confined excitations in quantum magnets as a powerful strategy to understand the nature of quantum spin many-body models.
High Resolution Image Download MS PowerPoint Slide Extracting the Hamiltonian parameters of nanoscale quantum magnets from experimental measurements is a significant challenge in quantum matter. Here we establish a machine learning strategy to extract the parameters of a spin Hamiltonian from inelastic spectroscopy with scanning tunneling microscopy, and we demonstrate this methodology experimentally with an artificial nanoscale molecular magnet based on cobalt phthalocyanine (CoPC) molecules on NbSe 2 . We show that this technique allows us to extract the Hamiltonian parameters of a quantum magnet from the differential conductance, including the substrate-induced spatial variation of the exchange couplings. Our methodology leverages a machine learning algorithm trained on exact quantum many-body simulations with tensor networks of finite quantum magnets, leading to a methodology that predicts the Hamiltonian parameters of CoPC quantum magnets of arbitrary size. Our results demonstrate how quantum many-body methods and machine learning enable us to learn a microscopic description of nanoscale quantum many-body systems with scanning tunneling spectroscopy.
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 scanning tunneling microscopy and the local imaging of excitations with electrically driven spin resonance has risen as a powerful strategy to image spin excitations in finite quantum spin systems. Here, focusing on S=1/2S=1/2S=1/2 lattices as realized by Ti\mathrm{Ti}Ti in MgO\mathrm{Mg}\mathrm{O}MgO, we show that dynamical spin excitations provide a robust strategy to infer the nature of the underlying Hamiltonian. We show that finite-size interference of the dynamical many-body spin excitations of a generalized long-range Heisenberg model allows the underlying spin couplings to be inferred. We show that the spatial distribution of local spin excitations in Ti\mathrm{Ti}Ti islands and ladders directly correlates with the underlying ground state in the thermodynamic limit. Using a supervised-learning algorithm, we demonstrate that the different parameters of the Hamiltonian can be extracted by providing the spatially dependent and frequency-dependent local excitations that can be directly measured by electrically driven spin resonance with scanning tunneling microscopy. Our results put forward local dynamical excitations in confined quantum spin models as versatile witnesses of the underlying ground state, providing an experimentally robust strategy for Hamiltonian inference in complex real spin models.
Characterizing quantum correlations in many-body systems is essential for understanding emergent phenomena in quantum materials. The correlation entropy serves as a key metric for assessing the complexity of a quantum many-body state in interacting electronic systems. However, its determination requires the measurement of all single-particle correlators across a macroscopic sample, which can be impractical. Machine learning methods have been shown to enable the correlation entropy to be learned from a reduced set of measurements, yet these methods assume that the targeted system is contained in the set of training Hamiltonians. Here we show that a transfer learning strategy enables correlation entropy learning from a reduced set of measurements in variants of Hamiltonians never considered in the training set. We demonstrate this transfer learning methodology in a wide variety of interacting models, including local and nonlocal attractive and repulsive many-body interactions, long-range hopping, doping, magnetic field, and spin-orbit coupling. Furthermore, we show that this transfer learning methodology enables the detection of phases in quantum many-body systems beyond those present in the training set. Our results demonstrate that correlation entropy learning can be performed experimentally without requiring training in the experimentally realized Hamiltonian.
To understand the intriguing many-body states and effects in the correlated quantum materials, inference of the microscopic effective Hamiltonian from experiments constitutes an important yet very challenging inverse problem. Here we propose an unbiased and efficient approach learning the effective Hamiltonian through the many-body analysis of the measured thermal data. Our approach combines the strategies including the automatic gradient and Bayesian optimization with the thermodynamics many-body solvers including the exact diagonalization and the tensor renormalization group methods. We showcase the accuracy and powerfulness of the Hamiltonian learning by applying it firstly to the thermal data generated from a given spin model, and then to realistic experimental data measured in the spin-chain compound copper nitrate and triangular-lattice magnet TmMgGaO4. The present automatic approach constitutes a unified framework of many-body thermal data analysis in the studies of quantum magnets and strongly correlated materials in general.