Engineering quantum matter with generative machine learning

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Volume Title

School of Science | Doctoral thesis (article-based) | Defence date: 2024-06-13

Date

2024

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Mcode

Degree programme

Language

en

Pages

142 + app. 80

Series

Aalto University publication series DOCTORAL THESES, 125/2024

Abstract

Quantum matter presents a rich landscape of emergent phenomena and exotic properties that are rare in natural compounds. This includes many-body systems such as topological insulators and unconventional superconductors. Understanding and characterizing these systems presents significant challenges due to their complexity and exotic behavior. In this dissertation, we explore the intersection of condensed matter theory, quantum matter, and artificial intelligence (AI). We demonstrate how machine learning (ML) can be used as a powerful tool for untangling complex problems in quantum many-body physics and go beyond conventional methods. Generative ML methods allow us to design complex quantum materials efficiently, optimize experimental parameters, uncover hidden correlations of quantum many-body systems, and bring together experiments and theoretical models. With this thesis, we aim to provide a complementary strategy to design exotic quantum phenomena, making a step towards future technological advancements in correlated quantum materials, materials science, and quantum computing.

Description

Supervising professor

Lado, Jose L., Prof., Aalto University, Department of Applied Physics, Finland

Keywords

Correlated quantum materials, generative machine learning, quantum many-body physics

Other note

Parts

  • [Publication 1]: Rouven Koch and Jose L. Lado. Neural network enhanced hybrid quantum many-body dynamical distributions. Physical Review Research, 3, 033102, July 2021.
    DOI: 10.1103/PhysRevResearch.3.033102 View at publisher
  • [Publication 2]: Rouven Koch and Jose L. Lado. Designing quantum many-body matter with conditional generative adversarial networks. Physical Review Research, 4, 033223, September 2022.
    DOI: 10.1103/PhysRevResearch.4.033223 View at publisher
  • [Publication 3]: Rouven Koch, David van Driel, Alberto Bordin, Jose L. Lado, and Eliska Greplova. Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain. Physical Review Applied, 20, 044081, October 2023.
    DOI: 10.1103/PhysRevApplied.20.044081 View at publisher
  • [Publication 4]: Netta Karjalainen, Zina Lippo, Guangze Chen, Rouven Koch, Adolfo O. Fumega, and Jose L. Lado. Hamiltonian Inference from Dynamical Excitations in Confined Quantum Magnets. Physical Review Applied, 20, 024054, August 2023.
    DOI: 10.1103/PhysRevApplied.20.024054 View at publisher
  • [Publication 5]: Maryam Khosravian, Rouven Koch, Jose L. Lado. Hamiltonian learning with real-space impurity tomography in topological moiré superconductors. Journal of Physics: Materials, 7, 015012 2024.
    DOI: 10.1088/2515-7639/ad1c04 View at publisher

Citation