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arXiv:2203.01764

Random Quantum Neural Networks (RQNN) for Noisy Image Recognition

Debanjan Konar, Erol Gelenbe A, Soham Bhandary, Aditya Das Sarma, Attila Cangi

Published Mar 3, 2022• 7 citations
arXivHTMLPDF

Abstract

Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation has been limited to deterministic digital systems that output probability distributions in lieu of stochastic behaviors of random spiking signals. We introduce the novel class of supervised Random Quantum Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with superposition state and amplitude encoding features, inspired by quantum information theory and the brain's spatial-temporal stochastic spiking property of neuron information encoding. We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of \emph{qubits}. Experiments on the MNIST, FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an average classification accuracy of $94.9\%$. Additionally, the experimental findings illustrate the proposed RQNN's effectiveness and resilience in noisy settings, with enhanced image classification accuracy when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise, which is useful for various applications, including computer vision in NISQ devices. The PyTorch code (https://github.com/darthsimpus/RQN) is made available on GitHub to reproduce the results reported in this manuscript.

DOI
10.48550/arxiv.2203.01764

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The application of classical Random Neural Networks (RNN) has been restricted to deterministic digital systems that generate probability distributions instead of the stochastic characteristics of random spiking signals. To optimize the utilization of the stochastic properties inherent in RNN, we propose a new category of supervised Random Quantum Neural Networks (RQNN) that incorporate a resilient training methodology. The RQNN under consideration utilizes a combination of classical and quantum algorithms, incorporating superposition state and angle encoding characteristics that draw inspiration from quantum information theory. Additionally, the model incorporates the stochastic random spiking property of neuron information encoding, which is known to exhibit spatial-temporal features similar to those observed in the brain. The proposed RQNN model has undergone thorough validation, relying on hybrid classical-quantum algorithms through a real IBM quantum processor. The proposed RQNN model is tested on the MNIST, Fashion-MNIST, and KMNIST datasets. The results indicate that it achieved an average classification accuracy of 59.6% when presented with unseen noisy test images. The experimental results demonstrate the efficacy and robustness of the proposed RQNN11The early version of the work is posted in arXiv: https://doi.org/10.48550/arXiv.2203.01764. in classifying noisy images that have not been previously encountered.
Recently, quantum machine learning has been ap-plied to stochastic-based modelling, promising that the inherent uncertainty in quantum computing will be a significant advan-tage, driving neuromorphic computing research to new heights. Spiking Neural Networks (SNNs) and their neuromorphic are gaining popularity due to their inherent ability to process spatial and temporal data. However, learning the interconnection weights is daunting due to the inherent stochastic characteristics of neuron signals and the inherent non-differentiable spike events in classical SNN. This paper introduces a supervised Deep Spiking Quantum Neural Network (DSQ-Net) using a hybrid quantum- classical architecture having the merits of amplitude encoding in a dressed quantum layer. A novel attempt has been made to obviate the challenges in training a classical deep SNN, assisted by a Variational Quantum Circuit (VQC) in the proposed hy-brid quantum-classical framework. The DSQ-Net has undergone thorough validation and benchmarking procedures using the PennyLane Quantum Simulator and the limited volume real IBM Quantum hardware. The experiments have been conducted on images from the MNIST, FashionMNIST, KMNIST, and CIFAR-I0 datasets. Classification accuracy has been reported in the high nineties for unseen noisy test images using the proposed DSQ-Net model on the quantum simulator. It outper-forms its classical counterpart (Deep Spiking Neural Networks), shallow Random Quantum Neural Networks (RQNN), Quantum Superposition-inspired Spiking Neural Networks (SQIN), ResNet- 18, and AlexNet. The PyTorch implementation of DSQ-Net is made available on Github11https://anonymous.4open.science/r/DSQ-Net-037E,
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
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.
Image classification is an important application for deep learning. With the advent of quantum technology, quantum neural networks (QNNs) have become the focus of research. Traditional deep learning-based image classification involves using a convolutional neural network (CNN) to extract features from the image and a multi-layer perceptron (MLP) network to create the decision boundaries. However, quantum circuits with parameters can extract rich features from images and also create complex decision boundaries. This paper proposes a hybrid QNN (H-QNN) model designed for binary image classification that capitalizes on the strengths of quantum computing and classical neural networks. Our H-QNN model uses a compact, two-qubit quantum circuit integrated with a classical convolutional architecture, making it highly efficient for computation on noisy intermediate-scale quantum (NISQ) devices that are currently leading the way in practical quantum computing applications. Our H-QNN model significantly enhances classification accuracy, achieving a 90.1% accuracy rate on binary image datasets. In addition, we have extensively evaluated baseline CNN and our proposed H-QNN models for image retrieval tasks. The obtained quantitative results exhibit the generalization of our H-QNN for downstream image retrieval tasks. Furthermore, our model addresses the issue of overfitting for small datasets, making it a valuable tool for practical applications.
This research presents a hybrid quantum-classical machine learning framework for medical image classification under noisy conditions using chest X-ray images. A classical Convolutional Neural Network (CNN) model and a hybrid quantum-classical model integrating variational quantum circuits were implemented using PyTorch and PennyLane. Gaussian noise was introduced during validation to evaluate model robustness under image perturbations. Experimental results demonstrated comparable performance between the classical CNN and the hybrid quantum-classical model, highlighting the potential of Quantum Machine Learning (QML) for future healthcare AI applications. GitHub Repository:https://github.com/saichandukundarapu/quantum-covid-detection