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International Conference on Advanced Communication Technology

Hybrid quantum inspired neural model for commodity price prediction

R.P Mahajan

Published Apr 7, 2011• 4 citations
OpenAlex

Abstract

Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attempt, towards commodities price prediction using this concept is evolved. The commodity price prediction initiates the use of QNN in financial engineering applications.

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Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attempt, towards commodities price prediction using this concept is evolved. The commodity price prediction initiates the use of QNN in financial engineering applications.
Quantum Neural Network (QNN) can improve upon the inadequacies of the classical neural network (CNN). The CNN requires a huge memory and needs more computational power. A new field of computation is emerging which integrates quantum computation with CNN. A quantum inspired hybrid model of quantum neurons and classical neurons is proposed. This paper details an approach, perhaps the first attempt; towards stock price prediction using this concept is evolved. The stock price prediction initiates the use of QNN in financial engineering applications
A new field of computation is emerging which integrates quantum and classical computation.This is applied to solve the financial engineering problem of portfolio selection.Hopfield neural network is used for portfolio selection.A quantum inspired hybrid model of quantum neurons and classical neurons is proposed for the prediction of stock prices.An effort is made, probably the first time to develop and use a hybrid quantum neural network for the prediction of stock prices.The suggested multilayer hybrid quantum neural network contains hidden layer of quantum neurons while the visible layer is of classical neurons.The asset distribution is done by a modified greedy algorithm.It is assumed that quantum computers when come into existence shall provide huge potential in the form of computational power and memory.Classical Neural networks(CNN) have shown tremendous acceptability in solving problems with non-linear formulations that requires huge processing power and large memory which a quantum computer can provide, when they will come into existence.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.
The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs become powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserves before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networks and quantum computation and designing quantum deep neural networks (QDNNs) for acceleration on Noisy Intermediate-Scale Quantum (NISQ) processors is also an important research problem. As one of the most widely used neural network architectures, convolutional neural network (CNN) remains to be accelerated by quantum mechanisms, with only a few attempts have been demonstrated. In this paper, we propose a new hybrid quantum-classical circuit, namely Quantum Fourier Convolutional Network (QFCN). Our model achieves exponential speed-up compared with classical CNN theoretically and improves over the existing best result of quantum CNN. We demonstrate the potential of this architecture by applying it to different deep learning tasks, including traffic prediction and image 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.
The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted by the scope of the hardware development. Nevertheless, many neural network algorithms had been proposed before GPUs became powerful enough for running very deep models. Similarly, quantum algorithms can also be proposed as knowledge reserve before real quantum computers are easily accessible. Specifically, taking advantage of both the neural networks and quantum computation and designing quantum deep neural networks (QDNNs) for acceleration on the Noisy Intermediate-Scale Quantum (NISQ) processors is also an important research problem. As one of the most widely used neural network architectures, convolutional neural network (CNN) remains to be accelerated by quantum mechanisms, with only a few attempts having been demonstrated. In this article, we propose a new hybrid quantum-classical circuit, namely, Quantum Fourier Convolutional Network (QFCN). Our model achieves exponential speedup compared with classical CNN theoretically and improves over the existing best result of quantum CNN. We demonstrate the potential of this architecture by applying it on different deep learning tasks, including traffic prediction and image classification.