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ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

Advance quantum based binary neural network learning algorithm

Om Prakash Patel, Aruna Tiwari

Published Jun 1, 2015• 2 citations
Publisher

Abstract

In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting and underfitting problem but also saves time. The connection weights have been decided by quantum way, it gives large space to select optimal weights. A new parameter that is quantum separability is introduced here which find optimal separability plane to classify input sample in quantum way. For each connection weights it searches for optimal separability plane. Thus the best separability plane is found out with respect to connection weights. This algorithm is tested with three benchmark data set and produces improved results than existing quantum inspired and other classification approaches.

DOI
10.1109/snpd.2015.7176181

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In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting and underfitting problem but also saves time. The connection weights have been decided by quantum way, it gives large space to select optimal weights. A new parameter that is quantum separability is introduced here which find optimal separability plane to classify input sample in quantum way. For each connection weights it searches for optimal separability plane. Thus the best separability plane is found out with respect to connection weights. This algorithm is tested with three benchmark data set and produces improved results than existing quantum inspired and other classification approaches.
In this paper a novel quantum based binary neural network learning algorithm is proposed. It forms three layer network structure. The proposed method make use of quantum concept for updating and finalizing weights of the neurons and it works for two class problem. The use of quantum concept form an optimized network structure. Also performance in terms of number of neurons and classification accuracy is improved. Same is compared with a quantum-based algorithm for optimizing artificial neural networks algorithm (QANN). It is found that there is improvement in the form of number of neurons at hidden layer, number of iterations, training accuracy and generalization accuracy.
Artificial neural networks (ANN) proved to be efficient in solving many problems for big data analytics using machine learning. The complex and non-linear features of the input data can be learned and generalized by ANN. In the big data era, enormous amounts of data arrive from multiple sources. A stage is expected to be reached where even supercomputers are likely inundated with the big data. Training an ANN in such a situation is a challenging task due to the size and dimension of the big data. Also, a large number of parameters are to be used and optimized in the network to learn the patterns and analyze such data. Quantum computing is emerging as a field that provides a solution to this problem as a quantum computer can represent data differently using qubits. Qubits on quantum computers can be used to detect the hidden patterns in data that are difficult for a classical computer to find. Hence, there exists a huge scope for application in the area of artificial neural networks. In this work, we primarily focused on training an artificial neural network using qubits as artificial neurons. The simulation results show that our quantum computing approach for ANN (QC ANN) is efficient when compared to classical ANN. The model with qubits as artificial neurons can learn the features of data using fewer parameters for a binary classification task. We demonstrate our experiment using a quantum simulator and optimization of the quantum parameters used in QC ANN is carried out on a classical computer.
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.
We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.
The QNN (quantum neural network) algorithmic approach is to interpreted the forward networking in the dataset. Alike, some classical dataset structure take input from the layer of qubit and directed to another layer of qubit. i.e. back propagation technique. The nature of QNN is an algorithmic based on parameter quantum circuit to train the dataset in an sequence manner by using classical optimizers to design an unique algorithmic computing technique in quantum learning, social network modeling, memory devices, automated control system, fast computing technique in executing program. It can be implemented in various sectors to learn hidden agenda layers -financial, health, economic etc. It decreases the limit of qubits, time & space complexity and compute within a second. It mainly optimizes the wave function, qubits representation in the QNN computing. The QNN is entitled for NISQ (noisy intermediate scale quantum computer) to remove the noise from the dataset. In machine learning dataset gather and trained in system with the model of QNN it enhanced the computing technique and optimizing strategies to remove noise from the data while modulating the signal approach. The layer of QNN introduces in this paper to approach the bit neural network, cellular network & associative neural network approach to classify the quantum learning optimization. This algorithmic approach implemented in the breast cancer dataset & predicts the 88.44 % accuracy on predicting the disease in patient. The aim of this paper is to categories the QNN properties in machine learning algorithm & provides new evidence of optimization.
Presents a quantum BP neural network model and its improved learning algorithm.Firstly a quantum neuron is constructed based on the universality of single qubit rotation gate and two-qubit controlled not gate.Secondly,the hide layer is constructed and uses these quantum neurons with the application of the gradient descent algorithm and the output layer is made with traditional neurons,in which weight and bias values are updated according to the improved algorithm of gradient descent momentum and an adaptive learning rate.It is shown that this model and algorithm are superior to the conventional BP networks in two aspects:convergence speed and accuracy by two UCI's data sets examples.