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Model of a quantum neural network based on a multilevel activation function

S.M. Gushansky, V.E. Buglov

Published Jan 1, 2022• 0 citations
Publisher

Abstract

This article proposes a model of a quantum neural network based on a multilevel activation function, which has high performance. This performance is achieved by combining such unique components as: the quantum weight vector, quantum activity values and the dot product operator. In addition, algorithms for learning weight parameters and quantum intervals are presented, in particular, the Levenberg-Marquardt algorithm is improved to improve the quality of the error back propagation method in the process of learning weight parameters, which will also reduce the likelihood of quantum errors.

DOI
10.34219/2078-8320-2022-13-2-44-48

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This article proposes a model of a quantum neural network based on a multilevel activation function, which has high performance. This performance is achieved by combining such unique components as: the quantum weight vector, quantum activity values and the dot product operator. In addition, algorithms for learning weight parameters and quantum intervals are presented, in particular, the Levenberg-Marquardt algorithm is improved to improve the quality of the error back propagation method in the process of learning weight parameters, which will also reduce the likelihood of quantum errors.
Abstract Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.
Inspired by the principle of superposing states in quantum mechanics,a new kind of feedforward neural network with single hidden layer was designed and named as Quantum Neural Networks.It has mainly been used for data classification.Multilevel activation functions is adopted in Quantum Neural Networks and a different objective function is used for the learning of quantum intervals of the multilevel hidden units;minimizing the class-conditional variances at the outputs of the hidden units.Quantum Neural Networks is endowed with a special ability of identifying uncertainty in data classification by its unique design.However,as different objective functions are used for the learning of synaptic weights and quantum intervals,conflict is observed in the iterative training process which will cause more iteration times for training and degrades the training results.Using the theory of Constrained Optimization,a new training algorithm is proposed in this paper by introducing penalty function into the steepest descent method.In the new training algorithm, the conflict is removed.Experimental results indicate that the new algorithm can increase the training speed and improve the performance of the trained network remarkably.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
The training algorithm and the structure of quantum neural networks (QNN) that based on multilevel activation function are presented in this paper. Aiming at the influence of the activation function of output layer and the phenomenon of error saturation in the training process on the output values and convergence property, a linear superposition of arctangent function is introduced in as hide layer activation function, and error saturation prevention (ESP) function is constructed to improve the convergence property of QNN. The results of simulation program show that the convergence property is improved obviously.
Quantum Neural Networks (QNNs) represent a paradigm shift in machine learning, leveraging the unique properties of quantum systems to potentially surpass classical neural networks. However, the inherent complexity of quantum systems presents significant challenges in designing efficient and adaptable architectures. This paper explores the integration of adaptive activation functions into QNNs to address this challenge. We propose a novel mechanism for dynamically adjusting activation function parameters, fostering increased efficiency and improved model performance. The core of our work lies in a system that employs a feedback loop, iteratively refining the activation function based on the network's internal state and the task at hand. This adaptive process significantly reduces computational costs compared to static activation functions. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing enhanced performance in a targeted task. The resulting QNN exhibits superior results in terms of accuracy and speed, highlighting the potential of this novel activation mechanism.
A quantum neural networks model with learning algorithm is presented. First, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and prompting. Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors, the linked weight and the activation value are Q-bits. On the basic of gradient descent algorithm, a learning algorithm is proposed. It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting optimum solution. Finally, the availability of the approach is illustrated with application of by adjusting PID controller parameters.