Designing quantum many-body matter with conditional generative adversarial networks

Koch, Rouven; Lado, José L.

doi:10.1103/physrevresearch.4.033223

Cited by 10 publications

(8 citation statements)

References 90 publications

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“…Learning Hamiltonian parameters from experimental data is one of the most critical open problems in order to bring together experiments with theoretical models [1][2][3][4][5][6][7][8]. Conventionally, phenomenological models to account for experimental data are developed on a case by case basis.…”

Section: Introductionmentioning

confidence: 99%

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Khosravian,

Koch,

Lado

2024

J. Phys. Mater.

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Extracting Hamiltonian parameters from available experimental data is a challenge in quantum materials. In particular, real-space spectroscopy methods such as scanning tunneling spectroscopy allow probing electronic states with atomic resolution, yet even in those instances extracting the effective Hamiltonian is an open challenge. Here we show that impurity states in modulated systems provide a promising approach to extracting non-trivial Hamiltonian parameters of a quantum material. We show that by combining the real-space spectroscopy of different impurity locations in a moiré topological superconductor, modulations of exchange and superconducting parameters can be inferred via machine learning. We demonstrate our strategy with a physically-inspired harmonic expansion combined with a fully-connected neural network that we benchmark against a conventional convolutional architecture. We show that while both approaches allow extracting exchange modulations, only the former approach allows inferring the features of the superconducting order. Our results demonstrate the potential of machine learning methods to extract Hamiltonian parameters by real-space impurity spectroscopy as local probes of a topological state.

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Section: Introductionmentioning

confidence: 99%

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Khosravian,

Koch,

Lado

2024

J. Phys. Mater.

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“…years, machine learning methods [29,30] have provided a complementary strategy to rationalize phases of matter, often in combination with conventional quantum many-body methods. The demonstrations of these roles played by machine learning methods in tackling many-body problems results in characterizing different phases of matter [31][32][33][34][35][36][37][38][39][40], deep learning of the quantum dynamics [41][42][43][44], obtaining many-body wave functions [45][46][47][48][49], and optimizing the performance of computational simulations [50].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Sayyad,

Lado

2024

J. Phys.: Condens. Matter

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Identifying phase boundaries of interacting systems is one of the key steps to understanding quantum many-body models. The development of various numerical and analytical methods has allowed exploring the phase diagrams of many Hermitian interacting systems. However, numerical challenges and scarcity of analytical solutions hinder obtaining phase boundaries in non-Hermitian many-body models. Recent machine learning methods have emerged as a potential strategy to learn phase boundaries from various observables without having access to the full many-body wavefunction. Here, we show that a machine learning methodology trained solely on Hermitian correlation functions allows identifying phase boundaries of non-Hermitian interacting models. These results demonstrate that Hermitian machine learning algorithms can be redeployed to non-Hermitian models without requiring further training to reveal non-Hermitian phase diagrams. Our findings establish transfer learning as a versatile strategy to leverage Hermitian physics to machine learning non-Hermitian phenomena.

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“…This technology has been instrumental across a broad range of scientific disciplines, including physics, chemistry, and biology. In physics, GANs have been used for simulating complex systems and predicting outcomes of experiments, with examples in high energy physics [29], condensed matter physics [30][31][32], nanophotonics [33,34], and cosmology [35]. In the field of chemistry, GANs have been harnessed to generate novel chemical structures and predict their properties [36], thereby accelerating the process of drug discovery and materials design [37].…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial networks for data-scarce radiative heat transfer applications

García-Esteban,

Cuevas,

Bravo-Abad

2024

Mach. Learn.: Sci. Technol.

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Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation for data-scarce radiative heat transfer applications, an area where their use has not been previously reported. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work contributes to highlight the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.

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Designing quantum many-body matter with conditional generative adversarial networks

Cited by 10 publications

References 90 publications

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Hamiltonian learning with real-space impurity tomography in topological moiré superconductors

Transfer learning from Hermitian to non-Hermitian quantum many-body physics

Generative adversarial networks for data-scarce radiative heat transfer applications

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