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Intelligent Autonomous Systems

Non-Equilibrium Dynamics of Quantum Many-Body Systems for Image Processing

Ruiyang Zhou, Abolfazl Bayat

Published Dec 26, 2025• 0 citations
Publisher

Abstract

In this work, we numerically demonstrate the performance of our recent hybrid classical-quantum algorithm for image classification using four different data sets. This hybrid algorithm combines a classical neural network with nonequilibrium dynamics of a many-body system. The goal is to benefit from the flexibility of neural networks in dealing with large feature sets and leverage the fast non-equilibrium manybody dynamics for discriminating quantum states. The classical neural network performs task-aware dimensionality reduction while its parameters are optimized according to a cost function which is obtained by a quantum module. The large input images are compressed to a very few numbers at the output of the neural network. Those numbers are used as the strength of local magnetic fields in a many-body system whose Hamiltonian is described by an Ising Hamiltonian in the transverse field. Thus, the dynamics of such system encodes every image into a quantum state. The classical neural network is optimized in such a way that the images from the same class are mapped to quantum states which have high fidelity with each other while images from different classes are mapped to orthogonal states. We numerically evaluate the approach on four datasets, including PathMNIST, BloodMNIST, OCTMNIST, and PneumoniaMNIST. For benchmarking, we compare the performance of the protocol against classification accuracy of a pure classical neural network whose complexity is comparable with the classical module in our protocol. Across datasets, the hybrid protocol consistently improves test accuracy, with the magnitude of gains depending on modality and task complexity. Increasing the number of qubits, neural network depth, and training size can enhance the accuracy of the protocol. Together, these results position quantum dynamics as an effective feature engine for computer vision, and outline design guidelines for resource-efficient hybrid protocols.

DOI
10.1109/icoias69065.2025.00008

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In this work, we numerically demonstrate the performance of our recent hybrid classical-quantum algorithm for image classification using four different data sets. This hybrid algorithm combines a classical neural network with nonequilibrium dynamics of a many-body system. The goal is to benefit from the flexibility of neural networks in dealing with large feature sets and leverage the fast non-equilibrium manybody dynamics for discriminating quantum states. The classical neural network performs task-aware dimensionality reduction while its parameters are optimized according to a cost function which is obtained by a quantum module. The large input images are compressed to a very few numbers at the output of the neural network. Those numbers are used as the strength of local magnetic fields in a many-body system whose Hamiltonian is described by an Ising Hamiltonian in the transverse field. Thus, the dynamics of such system encodes every image into a quantum state. The classical neural network is optimized in such a way that the images from the same class are mapped to quantum states which have high fidelity with each other while images from different classes are mapped to orthogonal states. We numerically evaluate the approach on four datasets, including PathMNIST, BloodMNIST, OCTMNIST, and PneumoniaMNIST. For benchmarking, we compare the performance of the protocol against classification accuracy of a pure classical neural network whose complexity is comparable with the classical module in our protocol. Across datasets, the hybrid protocol consistently improves test accuracy, with the magnitude of gains depending on modality and task complexity. Increasing the number of qubits, neural network depth, and training size can enhance the accuracy of the protocol. Together, these results position quantum dynamics as an effective feature engine for computer vision, and outline design guidelines for resource-efficient hybrid protocols.
The integration of quantum computing and machine learning has emerged as a promising frontier in computational science. We present a hybrid protocol which combines classical neural networks with non-equilibrium dynamics of a quantum many-body system for image classification. This architecture leverages classical neural networks to efficiently process high-dimensional data and encode it effectively on a quantum many-body system, overcoming a challenging task towards scaled up quantum computation. The quantum module further capitalizes on the discriminative properties of many-body quantum dynamics to enhance classification accuracy. By mapping images from distinct classes to nearly-orthogonal quantum states, the system maximizes separability in the Hilbert space, enabling robust classification. We evaluate the performance of our model on several benchmark datasets with various number of features and classes. Moreover, we demonstrate the key role of the quantum module in achieving high classification accuracy which cannot be accomplished by the classical neural network alone. This showcases the potential of our hybrid protocol for achieving practical quantum advantage and paves the way for future advancements in quantum-enhanced computational techniques.
Abstract Image classification is a fundamental problem in computer vision, and neural networks provide an effective solution. With the advancement of quantum technology, quantum neural networks have attracted a lot of attention. However, they are only suitable for low-dimensional data and require dimensionality reduction and quantum encoding. Two image classification methods have recently emerged: one employs PCA for dimensionality reduction and angular encoding, and the other integrates QNN into a CNN to improve performance. Despite numerous algorithms, the differences between them remain unclear. This study explores these algorithms’ performance in multi-class image classification and proposes an optimized hybrid quantum neural network suitable for the current environment. As the number of classes increases, research on PCA-based quantum algorithms reveals the barren plateau problem of QNN, which is not suitable for multiple classes in a hybrid setting. Our proposed model combining traditional CNN with QNN addresses QNN’s multi-class training difficulties to some extent and achieves satisfactory classification results. Nevertheless, its accuracy remains below that of the top-performing CNN models. Furthermore, we investigate transfer learning in hybrid quantum neural network models and assess the performance of our models on the quantum hardware from IBM. In conclusion, quantum neural networks show promise but require further research and optimization, facing challenges ahead.
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Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.
This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with ε=0.1ε=0.1ε=0.1 perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\%, 32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%) and STL10 (+10.29\%). Hybrid models also train 5--12×\times× faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but show comparable fragility on complex datasets like CIFAR100 ($\sim$1\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\% vs. 23.2\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.
In the last decade, quantum computing (QC) has shown its great potential in advancing research in many fields. Here we introduce a new quantum-classical neural network, by combining quantum computing and classical computing in a hybrid neural network (HNN) that can be trained to perform image classification. The HNN on the basis of the classic convolutional neural network (CNN) with quantum circuit is considered for image classification problem. The various configurations of HNN were investigated where QC with different number of qubits were used and compared. The HNN configurations were trained, validated, and tested on the more complex CIFAR10 and CIFAR100 datasets in addition to our previous attempts on the simpler MNIST, notMNIST, MNIST Fashion datasets. Performance of HNN was compared for multiclass classification on these datasets for different number of classes (from 2 to 10) using QCs with correspondent number of qubits (from 2 to 4). The metrics measured (accuracy and loss) during these experiments support our assumption about feasibility of HNN application for multiclass classification problems.