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.
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.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks
Abstract Leukemia is a life-threatening blood cancer that requires early and accurate diagnosis to improve patient outcomes. Traditional diagnostic methods based on manual examination of blood smear images are time-consuming and prone to human error. With the advancement of artificial intelligence, automated detection systems using machine learning and deep learning techniques have shown promising results. However, classical models face limitations in handling complex feature spaces efficiently. To address this, this project proposes a hybrid approach titled “Leukemia Detection using Quantum Machine Learning”, which integrates classical image processing with quantum computing techniques. In this work, microscopic blood smear images from a publicly available Acute Lymphoblastic Leukemia dataset are utilized. The images undergo preprocessing steps including resizing, noise removal using Gaussian filtering, and normalization to enhance data quality. A Convolutional Neural Network (CNN) is employed for classical feature extraction, transforming image data into meaningful feature vectors. These features are further reduced using Principal Component Analysis (PCA) to make them suitable for quantum processing. The reduced feature set is encoded into quantum states using both angle encoding and amplitude encoding techniques. Quantum properties such as superposition and entanglement are leveraged through parameterized quantum circuits. Two quantum machine learning models, namely Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN), are implemented to perform multi-class classification of leukemia stages, including Benign, Early, Pre, and Pro. The performance of both models is evaluated and compared using metrics such as accuracy, confusion matrix, and classification report. The proposed hybrid classical-quantum framework demonstrates the potential of quantum machine learning in medical image analysis. By combining deep learning with quantum algorithms, the system aims to improve diagnostic accuracy and computational efficiency. This approach not only enhances leukemia detection but also highlights the applicability of quantum computing in real-world healthcare problems, paving the way for future advancements in intelligent medical diagnosis systems.
This research presents a new hybrid model that combines the capabilities of a pre-trained ResNet-50 with a Quantum Neural Network (QNN) to enhance the classification of MRI brain images for tumor recognition. Combining conventional deep learning and quantum computing, this technique competes with traditional models regarding accuracy, speed, and computational effectiveness. The ResNet-50 architecture is the foundation for feature extraction, using its demonstrated ability to handle challenging MRI image data. ResNet-50's linear layer weights are converted into angular parameters to take advantage of the potential of quantum computing, allowing the quantum layer to function within a six-angle depth framework. This unique integration allows the model to efficiently execute binary classification and multi-class tumor detection. The hybrid model outperforms conventional models, providing quicker processing speeds and higher classification accuracy, especially when dealing with high-dimensional and noisy data. The model optimizes 512-dimensional data using four qubits, allowing for improved feature representation and better insights into complicated medical imaging patterns. The quantum layer's resistance to noise enhances the model's accuracy, establishing a new standard for medical diagnostic tools. This novel integration of classical and quantum approaches offers a significant advance in MRI-based tumor identification, providing higher performance and opening the path for next-generation medical imaging systems.
There is a major promise of extensive technical disturbance made by increasing convergence of two rapid development areas: Quantum Computing & Artificial Intelligence (AI). Unlike traditional computers, quantum computer use quantum -mechanical concepts as complication & superposition towards process data in a new way. Because of this, quantum computer machines can improve traditional systems towards solve certain types of problems. Machine learning (ML) algorithm is a sub -region of artificial intelligence aimed at automatically analyzing, interpreting & predicting procedures on a dataset. It is expected that the merger of AI & quantum calculation can quickly increase the processor power available for machine learning, which will accelerate processes such as data analysis, mimic quantum systems & handle complex customization problems. Medication development, material research & encryption abide some of the areas that benefit from the correction of quantum calculation of AI. among Quantum-Enhanced AI, the training machine learning models can endure very quick & more effective, so we can take problems that were impossible in the past. Pattern recognition, classification & decision -making artificial intelligence abide corners of applications; Quanta algorithms can also provide better ways for these features. Nevertheless, there abide still obstacles towards removing, such as creating special quantum algorithms for AI functions, ensuring that quantum systems can fix incorrectly, & make quantum machine scalable. Despite these challenges, the combination of AI & quantum calculation has the opportunity towards enter a new era of technical progress, which has not been able towards solve traditional data processing by answering major, complex problems so far.
Image classification has become a fundamental task in computer vision with applications in areas such as medical imaging, agriculture, environmental monitoring, and automated surveillance. Traditional machine learning techniques have achieved reasonable performance in classification tasks; however, they often struggle when dealing with high-dimensional and complex image datasets. Deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved image classification performance by automatically learning hierarchical feature representations. Despite these advancements, classical deep learning models may still face challenges related to computational complexity and large-scale data processing.In recent years, quantum machine learning has emerged as a promising paradigm that combines principles of quantum computing with classical machine learning techniques to enhance computational efficiency and model performance. This study proposes a hybrid quantum–classical framework for image classification that integrates a deep residual network (ResNet-50) with a Quantum Support Vector Machine (QSVM). The ResNet-50 model is employed as a feature extraction mechanism to capture high-level visual representations from image data. The extracted features are then reduced in dimensionality using Principal Component Analysis (PCA) to simplify the feature space and improve computational efficiency.The reduced feature vectors are subsequently classified using a QSVM model that utilizes quantum feature maps to encode classical data into quantum states. Various quantum feature maps are explored to evaluate their impact on classification performance. Experimental results demonstrate that the hybrid quantum–classical approach achieves higher classification accuracy compared to conventional machine learning models such as Support Vector Machines and Random Forest classifiers. The proposed framework highlights the potential of combining classical deep learning architectures with quantum machine learning algorithms to address complex image classification challenges. This hybrid approach provides an efficient and scalable solution for advanced image analysis tasks and demonstrates the growing potential of quantum computing in artificial intelligence applications.
In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.