Abstract Machine learning has been widely applied in physics research. Although unsupervised learning can extract the critical points of phase transitions, the percolation model remains a challenge. Unsupervised learning using the raw configurations of the percolation model fails to capture the critical points. To capture the configuration characteristics of the percolation model, this paper proposes using the maximum cluster as input to the neural network. It is well understood that the order parameter of the percolation model is not simply the particle density, but rather the probability that a given site or bond belongs to the percolating cluster. Additionally, we introduce the use of a Siamese Neural Network (SNN) to detect percolation phase transitions. Unlike unsupervised dimensionality reduction methods or supervised binary classification outputs, the SNN produces a scalar output referred to as similarity. By combining the maximum cluster and the SNN, we not only successfully extract the critical value of the percolation model, but also calculate the correlation exponent via data collapse. We believe that the SNN has great potential in handling phase transition classification problems and can serve as a reference for studying other phase transition systems.Abstract Machine learning has been widely applied in physics research. Although unsupervised learning can extract the critical points of phase transitions, the percolation model remains a challenge. Unsupervised learning using the raw configurations of the percolation model fails to capture the critical points. To capture the configuration characteristics of the percolation model, this paper proposes using the maximum cluster as input to the neural network. It is well understood that the order parameter of the percolation model is not simply the particle density, but rather the probability that a given site or bond belongs to the percolating cluster. Additionally, we introduce the use of a Siamese Neural Network (SNN) to detect percolation phase transitions. Unlike unsupervised dimensionality reduction methods or supervised binary classification outputs, the SNN produces a scalar output referred to as similarity. By combining the maximum cluster and the SNN, we not only successfully extract the critical value of the percolation model, but also calculate the correlation exponent via data collapse. We believe that the SNN has great potential in handling phase transition classification problems and can serve as a reference for studying other phase transition systems.

Physica ScriptaOct 10, 20242 citations

Qiamese: Quantum-Hybrid Siamese Network for Predicting Drug-Protein Interaction

Soham Pawar, Siddharth Ayathu, Naganand Y

Accurate prediction of drug-protein interactions (DPIs) is a pivotal step in drug discovery. Classical deep learning models like BridgeDPI [3] have shown strong performance by using Graph Neural Networks (GNNs) with hyper-nodes to effectively learn protein-protein and drug-drug associations (PPAs and DDAs). Siamese-based models such as BioAct-Het use a heterogeneous siamese neural network to model the complex relationship between drugs and bioactivity classes, bringing them into a unified latent space. The final bioactivity class label is decided based on the distance between the final embeddings obtained from the classical twin networks. In our work, we propose a parameter-efficient QuantumHybrid Siamese model where the protein and drug embeddings, generated by the classical encoders are processed on by the Quantum Siamese circuit, much like the classical Siamese model. Using a switch qubit, helps us implement the idea of weight sharing across the twin instances for the pair of inputs, but by using only a single instance of the VQC block. We focus on using our proposed circuit with the classical protein and drug encoders proposed in the BridgeDPI model. Our experiments include testing the model variations on the unseen drugprotein split of the BindingDB dataset as suggested by Gao et. al. from https://github.com/IBM/InterpretableDTIP [3]. Upon experimenting with different variations of the classical encoders and the quantum circuits, we achieved the best performing hybrid model (10 MB) with performance metrics comparable to the best performing classical model $(40 \text{MB})$ along with $4 x$ size reduction and 540 trainable quantum parameters. The code for this work can be found at https://github.com/SoardRaspi/Qiamese.Accurate prediction of drug-protein interactions (DPIs) is a pivotal step in drug discovery. Classical deep learning models like BridgeDPI [3] have shown strong performance by using Graph Neural Networks (GNNs) with hyper-nodes to effectively learn protein-protein and drug-drug associations (PPAs and DDAs). Siamese-based models such as BioAct-Het use a heterogeneous siamese neural network to model the complex relationship between drugs and bioactivity classes, bringing them into a unified latent space. The final bioactivity class label is decided based on the distance between the final embeddings obtained from the classical twin networks. In our work, we propose a parameter-efficient QuantumHybrid Siamese model where the protein and drug embeddings, generated by the classical encoders are processed on by the Quantum Siamese circuit, much like the classical Siamese model. Using a switch qubit, helps us implement the idea of weight sharing across the twin instances for the pair of inputs, but by using only a single instance of the VQC block. We focus on using our proposed circuit with the classical protein and drug encoders proposed in the BridgeDPI model. Our experiments include testing the model variations on the unseen drugprotein split of the BindingDB dataset as suggested by Gao et. al. from https://github.com/IBM/InterpretableDTIP [3]. Upon experimenting with different variations of the classical encoders and the quantum circuits, we achieved the best performing hybrid model (10 MB) with performance metrics comparable to the best performing classical model$(40 \text{MB})$along with$4 x$size reduction and 540 trainable quantum parameters. The code for this work can be found at https://github.com/SoardRaspi/Qiamese.

International Conference on Quantum Communications, Networking, and Computing (QCNC)Apr 6, 20260 citations

Unsupervised machine learning of phase transition in percolation

Wei Yu, Pin Lyu

Physica A Statistical Mechanics and its ApplicationsAug 14, 202017 citations

A labeling strategy for the identification of phase transitions employing deep neural networks

Xing Guo, Bo Yang

Abstract Recent advancements in deep neural networks have improved phase transition analysis. However, their performance depends heavily on the labeling method, and traditional approaches based on critical values for labeling lack physical interpretability and overlook the behaviors of different configurations under fluctuations. To address these issues, we propose a labeling strategy based on the average value of the observable at the critical point. In the percolation model, the threshold is determined by the average relative size of the largest connected cluster, while in the Ising model, it is based on the average absolute magnetization and energy. We validate the strategy on classical models, including site and bond percolation on 2D square and triangular lattices, bond percolation on Erdõs–Rényi (ER) random networks, and the Ising model on 2D square and triangular lattices. Convolutional neural networks are used for lattice data, and graph convolutional networks for ER network data. The results demonstrate that this observable-based labeling strategy can accurately identify critical points and exponents, offering universality and stability across various phase transition types.Abstract Recent advancements in deep neural networks have improved phase transition analysis. However, their performance depends heavily on the labeling method, and traditional approaches based on critical values for labeling lack physical interpretability and overlook the behaviors of different configurations under fluctuations. To address these issues, we propose a labeling strategy based on the average value of the observable at the critical point. In the percolation model, the threshold is determined by the average relative size of the largest connected cluster, while in the Ising model, it is based on the average absolute magnetization and energy. We validate the strategy on classical models, including site and bond percolation on 2D square and triangular lattices, bond percolation on Erdõs–Rényi (ER) random networks, and the Ising model on 2D square and triangular lattices. Convolutional neural networks are used for lattice data, and graph convolutional networks for ER network data. The results demonstrate that this observable-based labeling strategy can accurately identify critical points and exponents, offering universality and stability across various phase transition types.

Communications in Theoretical PhysicsMay 22, 20260 citations

Universality class of machine learning for critical phenomena

Gaoke Hu, Yu Sun, Teng Liu +5 more

Science China Physics Mechanics and AstronomyNov 17, 202312 citations

(40 \text{MB})

along with

4 x

size reduction and 540 trainable quantum parameters. The code for this work can be found at https://github.com/SoardRaspi/Qiamese.