Marc Machaczek
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Marc Machaczek has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Ke Liu
University of Science and Technology of China
2 shared publications - Lode Pollet
Ludwig-Maximilians-Universität München
2 shared publications - Nihal Rao
Department of Condensed Matter Physics, Weizmann Institute of Science, Rehovot 76100, Israel
1 shared publication
Latest Publications
- Open Access
- 28 March 2025
- Published by Stichting SciPost in SciPost Physics
- Vol. 18 (3) , 112
Neural quantum state study of fracton models
Fracton models host unconventional topological orders in three and higher dimensions and provide promising candidates for quantum memory platforms. Understanding their robustness against quantum fluctuations is an important task but also poses great challenges due to the lack of efficient numerical tools. In this work, we establish neural quantum states (NQS) as new tools to study phase transitions in these models. Exact and efficient parametrizations are derived for three prototypical fracton codes — the checkerboard and X-cube model, as well as Haah’s code — both in terms of a restricted Boltzmann machine (RBM) and a correlation-enhanced RBM. We then adapt the correlation-enhanced RBM architecture to a perturbed checkerboard model and reveal its strong first-order phase transition between the fracton phase and a trivial field-polarizing phase. To this end, we simulate this highly entangled system on lattices of up to 512 qubits with high accuracy, representing a cutting-edge application of variational neural-network methods. In addition, we reproduce the phase transition of the X-cube model previously obtained with quantum Monte Carlo and high-order series expansion methods. Our work demonstrates the remarkable potential of NQS in studying complicated three-dimensional problems and highlights physics-oriented constructions of NQS architectures.
- 28 March 2025
- Published by Stichting SciPost in SciPost Physics
- Vol. 18 (3) , 112
Open AccessNeural quantum state study of fracton models
Fracton models host unconventional topological orders in three and higher dimensions and provide promising candidates for quantum memory platforms. Understanding their robustness against quantum fluctuations is an important task but also poses great challenges due to the lack of efficient numerical tools. In this work, we establish neural quantum states (NQS) as new tools to study phase transitions in these models. Exact and efficient parametrizations are derived for three prototypical fracton codes — the checkerboard and X-cube model, as well as Haah’s code — both in terms of a restricted Boltzmann machine (RBM) and a correlation-enhanced RBM. We then adapt the correlation-enhanced RBM architecture to a perturbed checkerboard model and reveal its strong first-order phase transition between the fracton phase and a trivial field-polarizing phase. To this end, we simulate this highly entangled system on lattices of up to 512 qubits with high accuracy, representing a cutting-edge application of variational neural-network methods. In addition, we reproduce the phase transition of the X-cube model previously obtained with quantum Monte Carlo and high-order series expansion methods. Our work demonstrates the remarkable potential of NQS in studying complicated three-dimensional problems and highlights physics-oriented constructions of NQS architectures.
- Open Access
- 8 September 2021
- Published by American Physical Society (APS) in Physical Review Research
- Vol. 3 (3) , 033223
Machine-learned phase diagrams of generalized Kitaev honeycomb magnets
We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine, to investigate the low-temperature classical phase diagram of a generalized Heisenberg-Kitaev- () model on a honeycomb lattice. Aside from reproducing phases reported by previous quantum and classical studies, our machine finds a hitherto missed nested zigzag-stripy order and establishes the robustness of a recently identified modulated phase, which emerges through the competition between the Kitaev and spin liquids, against Heisenberg interactions. The results imply that, in the restricted parameter space spanned by the three primary exchange interactions—, , and , the representative Kitaev material lies close to the boundaries of several phases, including a simple ferromagnet, the unconventional , and nested zigzag-stripy magnets. A zigzag order is stabilized by a finite and/or term, whereas the four magnetic orders may compete in particular if is antiferromagnetic.- 8 September 2021
- Published by American Physical Society (APS) in Physical Review Research
- Vol. 3 (3) , 033223
Open AccessMachine-learned phase diagrams of generalized Kitaev honeycomb magnets
We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine, to investigate the low-temperature classical phase diagram of a generalized Heisenberg-Kitaev- () model on a honeycomb lattice. Aside from reproducing phases reported by previous quantum and classical studies, our machine finds a hitherto missed nested zigzag-stripy order and establishes the robustness of a recently identified modulated phase, which emerges through the competition between the Kitaev and spin liquids, against Heisenberg interactions. The results imply that, in the restricted parameter space spanned by the three primary exchange interactions—, , and , the representative Kitaev material lies close to the boundaries of several phases, including a simple ferromagnet, the unconventional , and nested zigzag-stripy magnets. A zigzag order is stabilized by a finite and/or term, whereas the four magnetic orders may compete in particular if is antiferromagnetic.