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arXiv:2303.03227 · Intelligent Computing

Parallel Hybrid Networks: An Interplay between Quantum and Classical Neural Networks

Mohammad Kordzanganeh, Daria Kosichkina, Alexey A. Melnikov

Published Jan 1, 2023• 26 citations
arXivHTMLPDF

Abstract

The use of quantum neural networks for machine learning is a paradigm that has recently attracted considerable interest. Under certain conditions, these models approximate the distributions of their datasets using truncated Fourier series. Owing to the trigonometric nature of this fit, angle-embedded quantum neural networks may have difficulty fitting nonharmonic features in a given dataset. Moreover, the interpretability of hybrid neural networks remains a challenge. In this study, we introduce an interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to (a) a classical multi-layered perceptron and (b) a variational quantum circuit, after which the 2 outputs are linearly combined. The quantum neural network creates a smooth sinusoidal foundation based on the training set, and the classical perceptrons fill the nonharmonic gaps in the landscape. We demonstrate this claim using 2 synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that parallel hybrid network architecture can improve solution optimality on periodic datasets with additional noise.

DOI
10.34133/icomputing.0028

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The use of quantum neural networks for machine learning is a paradigm that has recently attracted considerable interest. Under certain conditions, these models approximate the distributions of their datasets using truncated Fourier series. Owing to the trigonometric nature of this fit, angle-embedded quantum neural networks may have difficulty fitting nonharmonic features in a given dataset. Moreover, the interpretability of hybrid neural networks remains a challenge. In this study, we introduce an interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to (a) a classical multi-layered perceptron and (b) a variational quantum circuit, after which the 2 outputs are linearly combined. The quantum neural network creates a smooth sinusoidal foundation based on the training set, and the classical perceptrons fill the nonharmonic gaps in the landscape. We demonstrate this claim using 2 synthetic datasets sampled from periodic distributions with added protrusions as noise. The training results indicate that parallel hybrid network architecture can improve solution optimality on periodic datasets with additional noise.
Quantum Neural Networks (QNNs) represent a novel computational paradigm that merges the principles of quantum computing with the architecture of artificial neural networks. Through the quantum phenomena of superposition, entanglement, and interference, QNNs enable parallel computation in high-dimensional Hilbert spaces, offering the potential to surpass the representational limits of classical models. This review provides a comprehensive overview of the theoretical foundations and architectures of QNNs, including Quantum Perceptrons, Variational Quantum Circuits (VQCs), Quantum Convolutional Neural Networks (QCNNs), and Quantum Recurrent Neural Networks (QRNNs). Furthermore, it discusses hybrid quantum–classical training mechanisms and key challenges such as barren plateaus, decoherence, and sampling complexity. The review also highlights recent applications of QNNs in medical diagnostics, materials science, and financial forecasting, demonstrating their potential to accelerate computation and improve predictive accuracy. Finally, future research directions are discussed in relation to computational efficiency, model interpretability, and integration with next-generation quantum hardware.
Embedding of n-dimensional data in quantum space is an essential component in quantum computing realm. This research area is quite challenging with limited computational resources and complex quantum architecture. To overcome these problems, quantum approaches have been integrated with classical neural network. This hybrid model encodes the features using both angle encoding and amplitude encoding over quantum hilbert space, which is then processed through a Variational Quantum Circuit (VQC). These embedded features are fed to the classical neural network that enhances the performance of the traditional methods. A synthetic barcode and stripes (BAS) dataset was created for the verification of the proposed model. The results show that classical optimization methods, specifically gradient descent with momentum, achieve an accuracy of 82.38% for 4×4 data which is a significant improvement over existing researches that are limited to lower-dimensional data embedding.
In recent years, machine learning has achieved remarkable success in tasks such as image classification and solving complex real-world problems. The achievements of deep neural networks (DNNs) have led to the widespread adoption of block-based architectures. However, as the demand for Explainable AI (XAI) grows, there is a need for models that provide greater transparency and interpretability. Our work addresses this need by exploring the potential of quantum-classical hybrid models. By integrating classical neural networks (ClaNNs) with quantum neural networks (QNNs), we aim to develop AI systems that are not only powerful but also interpretable. We propose a framework that combines parameterized quantum circuits (PQCs) with classical layers to create a hybrid block design for QNNs. By adding a linear layer after the PQCs, the flexibility of the block design is increased. This architecture enhances the adaptability of QNN design and facilitates the integration of XAI principles through the comparison of the loss function planes. Our training method, inspired by knowledge distillation, addresses existing challenges in PQC-based QNN designs, providing insights into the optimization landscape. We validate the effectiveness of our hybrid block through experiments on standard datasets, demonstrating its potential to achieve comparable accuracy to traditional DNNs while providing improved interpretability.
This paper presents a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network. The quantum circuit is designed to encode the features of the Iris dataset using angle embedding and entangling gates, thereby capturing complex feature relationships that are difficult for classical models alone. The model, which we term a Quantum Convolutional Neural Network (QCNN), was trained over 20 epochs, achievinga perfect 100% accuracy on the Iris dataset test set on 16 epoch. Our results demonstrate the potential of quantum-enhanced models in supervised learning tasks, particularly in efficiently encoding and processing data using quantum resources. We detail the quantum circuit design, parameterizedgate selection, and the integration of the quantum layer with classical neural network components.This work contributes to the growing body of research on hybrid quantum-classical models and theirapplicability to real-world datasets.
Quantum Computing is a very promising paradigm given the ability that Quantum brings in terms of recognizing patterns which their classical counterparts very likely fail at.The intersection of Quantum Computing and Machine Learning gave rise to Quantum Machine Learning, which brings the power of Quantum mechanics to accelerate the progress of Machine Learning. However, Quantum Machine Learning still has its own set of challenges, given the state of Quantum Hardware currently. Here we propose QcNet: A Hybrid Quantum-Classical Neural Network which combines a tensor network with a parameterized quantum circuit. Here we modify the Quantum Neural Network by placing the parametrized quantum circuit convolutional layer at the top followed by some classical CNN layers to make it more feasible for the near term NISQ era and to make it scale to 43 classes. The Quantum Convolutional Layer extracts the higher level features and passes it to the classical layers for further processing. Our model is an end-to-end solution to train a quantum-style model over a multi-class supervised learning problem. This model pushes the limits beyond binary or few class classification and successfully performs well over multiclass classification tasks over 43 classes of the GTSRB dataset. Also it is highly flexible and can be adopted well in the NISQ era, considering the amount of quantum resources available.
Quantum machine learning has established as an interdisciplinary field to overcome limitations of classical machine learning and neural networks. This is a field of research which can prove that quantum computers are able to solve problems with complex correlations between inputs that can be hard for classical computers. This suggests that learning models made on quantum computers may be more powerful for applications, potentially faster computation and better generalization on less data. The objective of this paper is to investigate how training of quantum neural network (QNNs) can be done using quantum optimization algorithms for improving the performance and time complexity of QNNs. A classical neural network can be partially quantized to create a hybrid quantum-classical neural network which is used mainly in classification and image recognition. In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs). We encode the cost function of QNNs onto relative phases of a superposition state in the Hilbert space of the network parameters. The parameters are tuned with an iterative quantum approximate optimisation (QAOA) mixer and problem hamiltonians. VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets (more complex) which converges the computation in lesser time than QNN with decent training accuracy.