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Advanced materials research

Modeling of Proportional Integral Derivative Neural Networks Based on Quantum Computation

Dongxiang Nan, Yun Sheng Zhang, Xue Qiang Sun

Published Jun 1, 2011• 0 citations
Publisher

Abstract

There has been a growing interest in artificial neural networks (ANNs) based on quantum theoretical concepts and techniques due to cognitive science and computer science aspects. The so called Quantum Neural Networks (QNNs) are an exciting area of research in the field of quantum computation and quantum information. We proposed a modeling of Proportional integral derivative neural networks based on quantum computation called QNNs-PID that maps a nonlinear function. We analyze the main algorithms and architecture proposed the modeling of QNNs-PID. The main conclusion is that, up to now, we prove the feed back and back forward algorithm based on quantum computation how to use and give clearly the results for the nonlinear function in the context of QNNs-PID. We simulate an example to show the property of QNNs-PID in nonlinear systems.

DOI
10.4028/www.scientific.net/amr.267.757

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There has been a growing interest in artificial neural networks (ANNs) based on quantum theoretical concepts and techniques due to cognitive science and computer science aspects. The so called Quantum Neural Networks (QNNs) are an exciting area of research in the field of quantum computation and quantum information. We proposed a modeling of Proportional integral derivative neural networks based on quantum computation called QNNs-PID that maps a nonlinear function. We analyze the main algorithms and architecture proposed the modeling of QNNs-PID. The main conclusion is that, up to now, we prove the feed back and back forward algorithm based on quantum computation how to use and give clearly the results for the nonlinear function in the context of QNNs-PID. We simulate an example to show the property of QNNs-PID in nonlinear systems.
Quantum Neural Networks (QNN) is a burgeoning new field built upon the combination of classical neural networks and quantum computations, which has many problems needed to solve. The predictive model of QNN is an issue that must be settled to develop QNN based on proportional integral derivative neural networks and quantum computation, which can be so called generalized quantum neural networks (GQNN). Firstly, we describe the theory of quantum computation and neural networks. Secondly, it can realize the algorithm of prediction to construct the modeling of generalized quantum neural networks for those complexity nonlinear systems. Finally, using an example explains the model of generalized quantum neural networks. The computational results shows that GQNN is more effective than conventional neural networks.
Quantum computing enables quantum neural networks (QNNs) to have great potential to surpass artificial neural networks. The powerful generalization of neural networks is attributed to nonlinear activation functions. Although various models related to QNNs have been developed, they are facing the challenge of merging the nonlinear, dissipative dynamics of neural computing into the linear, unitary quantum system. In this paper, we establish different quantum circuits to approximate nonlinear functions and then propose a generalizable framework to realize any nonlinear quantum neuron. We present two quantum neuron examples based on the proposed framework. The quantum resources required to construct a single quantum neuron are polynomial in function of the input size. Finally, both IBM Quantum Experience results and numerical simulations illustrate the effectiveness of the proposed framework.
Artificial neural networks, usually just called neural networks, computing systems indefinitely inspired by the biological neural networks and they are extensive in both research as well as industry. It is critical to design quantum Neural Networks for complete quantum learning tasks. In this project, we suggest a computational neural network model based on principles of quantum mechanics which form a quantum feed-forward neural network proficient in universal quantum computation. This structure takes input from one layer of qubits and drives that input onto another layer of qubits. This layer of qubits evaluates this information and drives on the output to the next layer. Eventually, the path leads to the final layer of qubits. The layers do not have to be of the same breadth, meaning they need not have the same number of qubits as the layer before and/or after it. This assembly is trained on which path to take identical to classical ANN. The intended project can be compiled by the subsequent points provided here: 1. The expert training of the quantum neural network utilizing the fidelity as a cost function, providing both conventional and efficient quantum implementations. 2. Use of methods that enable quick optimization with reduced memory requirements. 3. Benchmarking our proposal for the quantum task of learning an unknown unitary and find extraordinary generality and a remarkable sturdiness to noisy training data.
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.
Over the past few decades, there has been a significant breakthrough in the field of quantum computing. Research is of growing interest, which has recently led to the development of quantum information systems prototypes and methods for their development. The paper describes the possibilities, advantages and disadvantages of quantum neural networks. The analysis of throughput, learning complexity and comparison of a classical feed-forward neural network and a quantum neural network with two different feature maps is carried out.