Quantum phase detection generalization from marginal quantum neural network models

Journal article

Oriel Kiss, Saverio Monaco, Antonio Mandarino, Sofia Vallecorsa, Michele Grossi
Phys. Rev. B, vol. 107(8), American Physical Society, 2023 Feb, pp. L081105

DOI: 10.1103/PhysRevB.107.L081105

Paper Code

Cite

APA Click to copy
Kiss, O., Monaco, S., Mandarino, A., Vallecorsa, S., & Grossi, M. (2023). Quantum phase detection generalization from marginal quantum neural network models. Phys. Rev. B, 107(8), L081105. https://doi.org/10.1103/PhysRevB.107.L081105

Chicago/Turabian Click to copy
Kiss, Oriel, Saverio Monaco, Antonio Mandarino, Sofia Vallecorsa, and Michele Grossi. “Quantum Phase Detection Generalization from Marginal Quantum Neural Network Models.” Phys. Rev. B 107, no. 8 (February 2023): L081105.

MLA Click to copy
Kiss, Oriel, et al. “Quantum Phase Detection Generalization from Marginal Quantum Neural Network Models.” Phys. Rev. B, vol. 107, no. 8, American Physical Society, Feb. 2023, p. L081105, doi:10.1103/PhysRevB.107.L081105.

BibTeX Click to copy

@article{kiss2023a,
  title = {Quantum phase detection generalization from marginal quantum neural network models},
  year = {2023},
  month = feb,
  issue = {8},
  journal = {Phys. Rev. B},
  pages = {L081105},
  publisher = {American Physical Society},
  volume = {107},
  doi = {10.1103/PhysRevB.107.L081105},
  author = {Kiss, Oriel and Monaco, Saverio and Mandarino, Antonio and Vallecorsa, Sofia and Grossi, Michele},
  month_numeric = {2}
}

Phase diagram of the ANNNI model predicted with a quantum convolutional neural network.

Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising Hamiltonian, which possesses a ferromagnetic, paramagnetic, and antiphase, showing that the whole phase diagram can be reproduced.

Quantum phase detection generalization from marginal quantum neural network models

Journal article

Cite

Follow this website