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Quantum Simulation and Communication Games

DAVID A. MEYER

Published Jul 1, 2006• 0 citations
OpenAlex

Abstract

Progress was made toward classification of different algorithmic schemes in quantum image processing. Goal one is to identify quantum versions of isotropic and anisotropic diffusion processing. Secondly, novel quantum transforms for feature extraction were sought. Finally, image classification in the quantum domain was to be investigated.

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Image classification is an important task in the field of machine learning and image processing. However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming. Especially in the era of big data, the problem is prominent when the amount of images to be classified is large. In this paper, we try to use the powerful parallel computing ability of quantum computers to optimize the efficiency of image classification. The scheme is based on quantum K Nearest-Neighbor algorithm. Firstly, the feature vectors of images are extracted on classical computers. Then the feature vectors are inputted into a quantum superposition state, which is used to achieve parallel computing of similarity. Next, the quantum minimum search algorithm is used to speed up searching process for similarity. Finally, the image is classified by quantum measurement. The complexity of the quantum algorithm is only O((kM)^(1/2)), which is superior to the classical algorithms. Moreover, the measurement step is executed only once to ensure the validity of the scheme. The experimental results show that, the classification accuracy is 83.1% on Graz-01 dataset and 78% on Caltech-101 dataset, which is close to existing classical algorithms. Hence, our quantum scheme has a good classification performance while greatly improving the efficiency.
Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for the required feature extraction and classification tasks. The classical networks use diffusion-based smearing and block-wise pooling to downsample the image data and capture important structural features. In this work, we propose and demonstrate a more efficient quantum-inspired strategy involving a mixture of experts. It is a hybrid classical-quantum framework. The quantum part consists of amplitude encoding of the images, convolution using local unitary operations, multiple experts processing the same image with different parameters, and feature extraction using quantum stabiliser codes. The classical part then jointly processes the features extracted by different experts using a standard fully connected neural network for image class prediction. Using MNIST and Fashion-MNIST datasets as benchmarks, we demonstrate that the joint expert analysis outperforms the individual expert one, as well as reduces the failure rate of image class prediction by around a factor of two. The overhead of our quantum-inspired strategy is only moderate on GPU workstations, which makes our proposal a practical alternative to existing classical schemes. We also point out how the quantum part of our framework can be executed on a quantum processor.
Quantum computing has made rapid progress in many fields, especially in artificial intelligence. Quantum machine learning is introduced to enhance the training rate of machine learning algorithms and hence, to achieve enhanced accuracy. The integration of quantum computing with artificial intelligence is incorporated with quantum neural networks and quantum tensor flow. Quantum neural networks play a vital role in enhancement of quantum computing progress in techniques of artificial intelligence. The aim of this special issue is to depict the importance and advances of quantum computing in a variety of fields, especially artificial intelligence. In the article by Jiao,1 fault tolerance for design of digital comparators is examined. A coplanar quantum-dot cellular automata (QCA) based fault tolerance comparator is proposed in order to achieve good simulation results. QCA designer needs 63 cells of QCA of 0.8μ2 and it requires three clock cycles delay to complete the task. In the article by Dogra et al.,2 an algorithm named multi-level filtering based image fusion algorithm is proposed for fusion of image information from MRI and CT scan images into a single vector. In the first step, source images are processed with the help of a filter of rolling guidance and then the calculation of the detail layer is performed. After that, guidance is used with the same rolling filter for the calculation of the base layer for edge preservation at a higher level. At the next step, in order to remove noise and other artifacts, all detail layers are fused together using the Karhunen Loeve transform and all the base layers are combined together by using the principle of weighted superimposition. In the article by Umer et al.,3 a method for early detection of COVID-19 is proposed in order to minimize the mortality rate. This method has two different phases: in the first phase, the pre-trained models like AlexNet and MobileNet are used and from their last fully connected layers deep features are extracted and then these extracted feature vectors are concatenated. Applying a method of feature selection, such as principal component analysis (PCA), the best features are then selected and then those features are passed to k-nearest neighbors algorithm (KNN) and support vector machine (SVM) for classification. In the second phase, a model of quantum transfer learning is used where a pre-trained model ResNet-18 is used for the collection of deep features and then these features are given to a four-qubit quantum circuit for the purpose of training with tuned hyper parameters. The proposed method is tested on two X-ray image datasets which are publicly available. In the article by Das et al.,4 the prediction of the OS period in a brain tumor is performed using the rain forest framework. Different algorithms such as eXtreme Gradient Boosting (XGBoost), light gradient boosting machine, and SVMs are used to take radiomic features and the efficiency of prediction is dependent upon the tumor volume, which is segmented after taking multiple MRI images. The complete tumor and its sub-parts are extracted from MRI model modalities by using the U-Net++ deep model and these are stacked together for the extraction of deep features via a convolutional neural network. In order to achieve increased accuracy, features are reduced by using PCA and then for the prediction of the OS period, the reduced radiomic feature set is used. The prediction results are tested for survival groups in both cases: for two classes and for three classes. Experiments are performed on the dataset of BraTs 2017 for both two classes and three classes by using different types of classifiers. In order to have an enhanced accuracy, different methods of optimization like PCA and genetic algorithm are used for the fusion of feature sets. In the article by Lepcha et al.,5 a piecewise regression model is proposed for learning the images through the mapping of resolution images from low to high. This is performed by utilizing filtering of the domain transform and an optimized framework of weighted least squares. First, the weighted least mean square is used to coarsen the given input images to extract multiscale information by building multiscale decompositions for edge preservations for which recursive filtering is also applied. The patterns named as Hadamard from low resolution images are collected for the classification of high resolution and low resolution image features. Finally, a piecewise linear regression model is used to learn the relationship of mapping between the classes of high resolution and low resolution image features.