Unsupervised learning of interacting topological and symmetry-breaking phase transitions

Kuo, En-Jui; Dehghani, Hossein

doi:10.1103/physrevb.105.235136

Cited by 5 publications

(3 citation statements)

References 63 publications

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“…Figure 5(d) shows the first derivative of the CrossArea with respect to the temperature, and the estimated phase transition points from the maximum gradient are T c /J = 1.0 and T c /J = 0.9 for L = 16 and L = 32, respectively. Note that similar to other unsupervised machine learning methods for phase transition detection [18,19,21,[48][49][50][51][52], a detailed error estimation on the predicted T c is not available. However, the convergence of the predicted T c as the system size increases suggests the validity of the method.…”

Section: Resultsmentioning

confidence: 99%

Rapid measurements and phase transition detections made simple by AC-GANs

Ding,

Tang,

2024

SciPost Phys. Core

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In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited. Here, we investigate classical many-body systems using generative adversarial neural networks. We employ the conditional generative adversarial network with an auxiliary classifier (AC-GAN) and integrate self-attention layers into the generator. This modification enables the network learn the distribution of the two-dimensional (2D) XY model’s spin configurations and the physical quantities of interest. Utilizing the symmetry of the systems, we discover that AC-GAN can be trained with a very small raw dataset. This approach allows us to obtain reliable measurements for models typically demanding large samples, such as the large-sized 2D XY and the 3D constrained Heisenberg models. Moreover, we demonstrate the capability of AC-GAN to identify the phase transition points by quantifying the distribution changes in the spin configurations of the systems.

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Section: Resultsmentioning

confidence: 99%

Rapid measurements and phase transition detections made simple by AC-GANs

Ding,

Tang,

2024

SciPost Phys. Core

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show abstract

“…Figure 5 (d) shows the first derivative of the CrossArea with respect to the temperature and the estimated phase transition point from the maximum gradient is T c = 1.0 and T c = 0.9 for L = 16 and L = 32, respectively. Note that similar to other unsupervised machine learning methods of phase transition detection [18,19,21,[47][48][49][50][51], a detailed error estimation on the predicted T c is not available. However, the convergence of the predicted T c as the system size increases suggests the validity of the method.…”

Section: Resultsmentioning

confidence: 99%

Report on scipost_202401_00034v1

2024

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“…We also highlight that our empirical complexity results raise interesting questions about the complexity of learning an effective Hamiltonian description 32 , 62 , 63 of the measurement outcome distributions for monitored quantum systems. Finally, we note that improving our neural network algorithms to find the optimal decoder and investigating the applicability of unsupervised machine learning techniques for this problem is left for future studies 64 , 65 .…”

Section: Discussionmentioning

confidence: 99%

Neural-network decoders for measurement induced phase transitions

et al. 2023

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Open quantum systems have been shown to host a plethora of exotic dynamical phases. Measurement-induced entanglement phase transitions in monitored quantum systems are a striking example of this phenomena. However, naive realizations of such phase transitions requires an exponential number of repetitions of the experiment which is practically unfeasible on large systems. Recently, it has been proposed that these phase transitions can be probed locally via entangling reference qubits and studying their purification dynamics. In this work, we leverage modern machine learning tools to devise a neural network decoder to determine the state of the reference qubits conditioned on the measurement outcomes. We show that the entanglement phase transition manifests itself as a stark change in the learnability of the decoder function. We study the complexity and scalability of this approach in both Clifford and Haar random circuits and discuss how it can be utilized to detect entanglement phase transitions in generic experiments.

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Unsupervised learning of interacting topological and symmetry-breaking phase transitions

Cited by 5 publications

References 63 publications

Rapid measurements and phase transition detections made simple by AC-GANs

Rapid measurements and phase transition detections made simple by AC-GANs

Report on scipost_202401_00034v1

Neural-network decoders for measurement induced phase transitions

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