Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters

• URL: http://arxiv.org/abs/2411.02237v1
• Date: Mon, 04 Nov 2024 16:30:58 GMT
• Title: Speak so a physicist can understand you! TetrisCNN for detecting phase transitions and order parameters
• Authors: Kacper Cybiński, James Enouen, Antoine Georges, Anna Dawid,
• Abstract summary: TetrisCNN is a convolutional NN with parallel branches using different kernels that detects the phases of spin systems. We show 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.
• Score: 3.669506968635671
• License:
• 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.

Related papers

• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Neural Networks as Spin Models: From Glass to Hidden Order Through Training [0.0]
  We explore a one-to-one correspondence between a neural network (NN) and a statistical mechanical spin model. We study the magnetic phases and the melting transition temperature as training progresses.
  arXiv  Detail & Related papers  (2024-08-12T18:01:04Z)
• Enforcing exact permutation and rotational symmetries in the application of quantum neural network on point cloud datasets [0.0]
  Recent developments in the field of quantum machine learning have promoted the idea of incorporating physical symmetries in the structure of quantum circuits. We provide a novel structure of QNN that is exactly invariant to both rotations and permutations.
  arXiv  Detail & Related papers  (2024-05-18T02:40:30Z)
• Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features [68.3512123520931]
  We investigate the dynamics of a deep neural network (DNN) learning interactions. In this paper, we discover the DNN learns interactions in two phases. The first phase mainly penalizes interactions of medium and high orders, and the second phase mainly learns interactions of gradually increasing orders.
  arXiv  Detail & Related papers  (2024-05-16T17:13:25Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks [1.9404281424219032]
  Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. We propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preserving perturbations.
  arXiv  Detail & Related papers  (2022-11-21T19:00:04Z)
• Theory for Equivariant Quantum Neural Networks [0.0]
  We present a theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. Our framework can be readily applied to virtually all areas of quantum machine learning.
  arXiv  Detail & Related papers  (2022-10-16T15:42:21Z)
• Interrelation of equivariant Gaussian processes and convolutional neural networks [77.34726150561087]
  Currently there exists rather promising new trend in machine leaning (ML) based on the relationship between neural networks (NN) and Gaussian processes (GP) In this work we establish a relationship between the many-channel limit for CNNs equivariant with respect to two-dimensional Euclidean group with vector-valued neuron activations and the corresponding independently introduced equivariant Gaussian processes (GP)
  arXiv  Detail & Related papers  (2022-09-17T17:02:35Z)
• Phase Detection with Neural Networks: Interpreting the Black Box [58.720142291102135]
  Neural networks (NNs) usually hinder any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to predict the phases of the one-dimensional extended spinless Fermi-Hubbard model at half-filling.
  arXiv  Detail & Related papers  (2020-04-09T17:45:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.