REVIEW 4 cited by
Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Recently, neural networks (NNs) have become a powerful tool for detecting quantum phases of matter. Unfortunately, NNs are black boxes and only identify phases without elucidating their properties. Novel physics benefits most from insights about phases, traditionally extracted in spin systems using spin correlators. Here, we combine two approaches and design TetrisCNN, a convolutional NN with parallel branches using different kernels that detects the phases of spin systems and expresses their essential descriptors, called order parameters, in a symbolic form based on spin correlators. We demonstrate this on the example of snapshots of the one-dimensional transverse-field Ising model taken in various bases. We show also that TetrisCNN can detect more complex order parameters using the example of two-dimensional Ising gauge theory. This work can lead to the integration of NNs with quantum simulators to study new exotic phases of matter.
Forward citations
Cited by 4 Pith papers
-
Time-Incremented Multiscale Evolution (TIME): A Code-Independent Method for Time-Domain 3D Hydrodynamics and its Application to Roche Lobe Overflow
A new TIME method for 3D hydrodynamic simulations is claimed to produce the first time-domain 3D model of Roche lobe overflow and a critical overfill factor f ~ 1.01 separating stable from unstable mass transfer in M33 X-7.
-
Opportunities and limitations of explaining quantum machine learning
The paper introduces two new explanation methods for quantum machine learning models (Taylor-∞ and QLRP) and reviews the field.
-
Learning interactions between Rydberg atoms
A size-invariant graph neural network, trained on small DMRG-simulated Rydberg Ising arrays, predicts atom positions on larger arrays from spin correlators, backed by a bijection theorem between correlations and interactions.
-
Machine learning applications in cold atom quantum simulators
A review of machine learning applications in cold atom quantum simulation, covering data analysis, experimental control, and state reconstruction.
Discussion (0). Continue with ORCID to comment.