Details
| Original language | English |
|---|---|
| Article number | 075146 |
| Journal | Physical Review B |
| Volume | 107 |
| Issue number | 7 |
| Publication status | Published - 22 Feb 2023 |
Abstract
We present a binary classifier to detect gapped quantum phases based on neural networks. By considering the errors on top of a suitable reference state describing the gapped phase, we show that a neural network trained on the errors can capture the correlation between the errors and can be used to detect the phase boundaries of the gapped quantum phase. We demonstrate the application of the method for matrix product state calculations for different quantum phases exhibiting local symmetry-breaking order, symmetry-protected topological order, and intrinsic topological order.
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
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In: Physical Review B, Vol. 107, No. 7, 075146, 22.02.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning of error statistics for the detection of quantum phases
AU - Jamadagni, Amit
AU - Kazemi, Javad
AU - Weimer, Hendrik
N1 - Funding Information: This work was funded by the Volkswagen Foundation, by the Quantum Valley Lower Saxony (QVLS) through the Volkswagen Foundation and the Ministry for Science and Culture of Lower Saxony, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within SFB 1227 (DQ-mat, Project No. A04), SPP 1929 (GiRyd), and under Germany's Excellence Strategy – EXC-2123 QuantumFrontiers – 390837967.
PY - 2023/2/22
Y1 - 2023/2/22
N2 - We present a binary classifier to detect gapped quantum phases based on neural networks. By considering the errors on top of a suitable reference state describing the gapped phase, we show that a neural network trained on the errors can capture the correlation between the errors and can be used to detect the phase boundaries of the gapped quantum phase. We demonstrate the application of the method for matrix product state calculations for different quantum phases exhibiting local symmetry-breaking order, symmetry-protected topological order, and intrinsic topological order.
AB - We present a binary classifier to detect gapped quantum phases based on neural networks. By considering the errors on top of a suitable reference state describing the gapped phase, we show that a neural network trained on the errors can capture the correlation between the errors and can be used to detect the phase boundaries of the gapped quantum phase. We demonstrate the application of the method for matrix product state calculations for different quantum phases exhibiting local symmetry-breaking order, symmetry-protected topological order, and intrinsic topological order.
UR - http://www.scopus.com/inward/record.url?scp=85149625532&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2205.12966
DO - 10.48550/arXiv.2205.12966
M3 - Article
AN - SCOPUS:85149625532
VL - 107
JO - Physical Review B
JF - Physical Review B
SN - 2469-9950
IS - 7
M1 - 075146
ER -