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![Figure 5. QNN based on VQA framework [63].](profile/Kyriaki-Tychola/publication/371001049/figure/fig5/AS:11431281161129038@1684938582296/QNN-based-on-VQA-framework-63_Q320.jpg)
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve...
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Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image...
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... Furthermore, quantum computing offers the promise of more efficient algorithms for a wide range of applications, from drug discovery and materials science to financial modeling and machine learning. Quantum algorithms can outperform their classical counterparts in speed and computational complexity by harnessing the power of quantum parallelism and entanglement (Tychola, Kalampokas, & Papakostas, 2023). ...
This review paper examines the potential implications of quantum computing for financial risk management. It explores the fundamental principles of quantum computing, including qubits, superposition, and entanglement. It discusses its advantages over classical computing for risk assessment and mitigation. The paper outlines traditional approaches to financial risk management. It explores how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance these strategies. Challenges and barriers to adopting quantum computing in the financial industry are identified, along with future research directions. Ultimately, the paper highlights the transformative potential of quantum computing for improving risk management in today's complex financial markets. Keywords: Quantum Computing, Financial Risk Management, Qubits, Quantum Algorithms, Monte Carlo Simulations, Portfolio Optimisation.
... Quantum computing, an area that was previously confined to theoretical physics, has seen rapid developments and is now regarded as a game-changing innovation in various industries, including telecommunications. Leveraging principles of quantum mechanics, quantum computers surpass classical counterparts by executing computations exponentially faster and with superior data processing capabilities [15]. The integration of QC into 6 G wireless communication technology signifies a revolutionary shift that promises unprecedented enhancements in data rates, reliability, and security [16]. ...
Context: The emergence of 6 G technology heralds a groundbreaking era in digital connectivity, envisaging universal and seamless links. To address the intricate computational and security requirements of this revolution, the integration of quantum computing (QC) into these networks is perceived as a promising solution. Objective: The objective this study presents a comprehensive investigation into the potential roles and implications of QC within the context of 6 G technology. Methodology: To address the objectives of this study, firstly, we have conducted literature survey to identify the key applications of using QC in 6 G technology. Secondly, we performed interview study with industry experts to identify the best practices related to the key application of QC in 6 G technology. Results: Our study unfolds in two distinct stages: firstly, we identify 15 key applications of QC in 6 G technology and segmented into 4 core areas. Secondly, the literature findings were empirically validated by conducting interview study and identified 49 best practices related to one of the identified key applications of QC in 6 G technology. Conclusion: The outcomes of this research lay a solid foundation for understanding both the pivotal applications of QC in 6 G technology and the effective practices for its implementation, thus providing valuable insights to both academics and industry practitioners.
... However, as traditional algorithms may not be sufficient, it is necessary to develop novel quantum algorithms. Shor's approach is capable of efficiently factoring large integers by utilizing quantum parallelism [186]. QML finds applications in diverse domains such as image processing, computational biology, bioinformatics, particle physics, communication networks, and privacy protection. ...
This review paper examines the crucial role of nanowires in the field of quantum computing, highlighting their importance as versatile platforms for qubits and vital building blocks for creating fault-tolerant and scalable quantum information processing systems. Researchers are studying many categories of nanowires, including semiconductor, superconducting, and topo-logical nanowires, to explore their distinct quantum features that play a role in creating various qubit designs. The paper explores the interdisciplinary character of quantum computing, combining the fields of quantum physics and materials science. This text highlights the significance of quantum gate operations in manipulating qubits for computation, thus creating the toolbox of quantum algorithms. The paper emphasizes the key research areas in quantum technology , such as entanglement engineering, quantum error correction, and a wide range of applications spanning from encryption to climate change modeling. The research highlights the importance of tackling difficulties related to decoding mitigation, error correction, and hardware scalability to fully utilize the transformative potential of quantum computing in scientific, technical , and computational fields.
... Two of the most exciting areas in computer science are quantum data processing and machine learning (Tychola et al., 2023). With the ability to use the laws of quantum physics to solve complicated computational problems, Quantum technology can change an array of various sectors ten times faster than regular computers. ...
Using quantum algorithms to improve deep learning models' capabilities is becoming increasingly popular as quantum computing develops. In this work, we investigate how quantum algorithms using quantum neural networks (QNNs) might enhance the effectiveness and performance of deep learning models. We examine the effects of quantum-inspired methods on tasks, including regression, sorting, and optimization, by thoroughly analyzing quantum algorithms and how they integrate with deep learning systems. We experiment with Estimator QNN and Sampler QNN implementations using Qiskit machine-learning, analyzing their forward and backward pass outcomes to assess the effectiveness of quantum algorithms in improving deep learning models. Our research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning. This work adds to the continuing investigation of quantum computing's potential to advance machine learning and artificial intelligence paradigms by clarifying the interaction between quantum algorithms and deep learning systems.
... QML emerges as a promising field, combining the principles of quantum computing with traditional machine learning techniques to address realworld challenges more efficiently. The works by Tychola et al. (2023) and Winker et al. (2023) [12], [13] explore QML, a field that merges quantum computing with conventional machine learning methods to tackle realworld challenges with increased efficiency. ...
... QML emerges as a promising field, combining the principles of quantum computing with traditional machine learning techniques to address realworld challenges more efficiently. The works by Tychola et al. (2023) and Winker et al. (2023) [12], [13] explore QML, a field that merges quantum computing with conventional machine learning methods to tackle realworld challenges with increased efficiency. ...
Quantum Convolutional Neural Networks (QCNNs) offer a promising avenue for image classification tasks due to their potential to leverage quantum properties for enhanced computational capabilities. In this paper, we explore the application of QCNNs for line orientation classification in pixelated images. Specifically, we investigate the differentiation between horizontal and vertical lines, a fundamental task in image processing and computer vision. We propose a QCNN architecture tailored to this task, leveraging quantum convolutional layers to extract features from pixelated images and classify line orientations. We demonstrate the effectiveness of our approach through experimental evaluation on benchmark datasets, comparing the performance of QCNNs with different optimizers. Our study integrated the QCNN operator and optimizer into Qiskit Machine Learning’s Neural Network Classifier, leveraging quantum computing techniques for classification tasks. Our QCNN model demonstrated a training accuracy of 71.43% and a test accuracy of 60.0%. Noteworthy observations include the failure of the SPSA optimizer to converge within the designated iterations, requiring twice the iterations compared to the COBYLA optimizer for convergence.
... email address for correspondence: maniraman.periyasamy@iis.fraunhofer.de learning tasks [12], [13], and are augmented by the promise of generalization based on fewer data points than classical methods [14], or improved accuracy [15] (albeit achieving concrete practical advantages over established classical heuristics is a subject of intense debate [16]) Current noisy intermediate-scale quantum (NISQ) devices in their earlier stages of development suffer from limited qubit counts, short coherence times, and high gate-and readouterror rates [17], [18]. This imposes restrictions on the size and complexity of quantum circuits, limiting algorithmic design choices. ...
... This is due to their ability to perform complex linear algebra computations, which are the core of SVMs, exponentially faster on quantum processors. QSVMs offer a promising avenue to not only match but potentially surpass the accuracy of classical SVMs, with the added benefit of reduced computational times [12,13]. ...
This study addresses the urgent need for improved prostate cancer detection methods by harnessing the power of advanced technological solutions. We introduce the application of Quantum Support Vector Machine (QSVM) to this critical healthcare challenge, showcasing an enhancement in diagnostic performance over the classical Support Vector Machine (SVM) approach. Our study not only outlines the remarkable improvements in diagnostic performance made by QSVM over the classic SVM technique, but it delves into the advancements brought about by the quantum feature map architecture, which has been carefully identified and evaluated, ensuring it aligns seamlessly with the unique characteristics of our prostate cancer dataset. This architecture succeded in creating a distinct feature space, enabling the detection of complex, non-linear patterns in the data. The findings reveal not only a comparable accuracy with classical SVM (92%) but also a 7.14% increase in sensitivity and a notably high F1-Score (93.33%). This study's important combination of quantum computing in medical diagnostics marks a pivotal step forward in cancer detection, offering promising implications for the future of healthcare technology.
... It can be used for financial interest rate modeling [89]. It has also been applied to develop financial models, such as churn prediction and credit risk assessment, where it has demonstrated better performance compared to traditional methods [101]. Quantum computing offers significant benefits in terms of computational speed and accuracy, making it a valuable tool in the finance field [102]. ...
Quantum computing, a transformative field that emerged from quantum mechanics and computer science, has gained immense attention for its potential to revolutionize computation. This paper aims to address the fundamentals of quantum computing and provide a comprehensive guide for both novices and experts in the field of quantum computing. Beginning with the foundational principles of quantum computing, we introduce readers to the fundamental concepts of qubits, superposition, entanglement, interference, and noise. We explore quantum hardware, quantum gates, and basic quantum circuits. This study offers insight into the current phase of quantum computing, including the noisy intermediate-scale quantum (NISQ) era and its potential for solving real-world problems. Furthermore, we discuss the development of quantum algorithms and their applications, with a focus on famous algorithms like Shor’s algorithm and Grover’s algorithm. We also touch upon quantum computing’s impact on various industries, such as cryptography, optimization, machine learning, and material science. By the end of this paper, readers will have a solid understanding of quantum computing’s principles, applications, and the steps involved in developing quantum circuits. Our goal is to provide a valuable resource for those eager to embark on their quantum computing journey and for researchers looking to stay updated on this rapidly evolving field.
... One of the recent works in QSVM was proposed by Jiang et al. in 2023 [25], which introduced a quantum algorithm for SVM classification that leverages the quantum phase estimation algorithm to estimate the kernel matrix. This approach leads to significant speedup compared to classical SVM algorithms, making QSVM a more efficient choice for large-scale datasets, which was also supported by Tychola et al. [26], and the authors provide a comparative analysis of the SVM with the QSVM by benchmarking several heuristic metrics. The results demonstrate the superiority of the QSVM over the classical SVM. ...
We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research.
... Comparison of quantum computing and machine learning. Quantum computing relies on the principles of quantum mechanics, using qubits to represent data and perform complex calculations Machine learning is a subset of artificial intelligence, focused on algorithms that learn from data to improve their performance over time 2. Hardware44 Specialized quantum computers or simulators are required, such as IBM's Quantum Experience, Google's Sycamore, and Rigetti's Aspen General-purpose computers, including CPUs, GPUs, and TPUs, can be used for machine learning tasks 3. Data representation45 Qubits can exist in multiple states simultaneously, enabling the exploration of a vast solution space Data is represented using classical bits, which can only exist in one state at a time 4. Speed46 Quantum algorithms can potentially solve certain problems significantly faster than classical algorithms, such as factorization and search Classical algorithms are generally faster for most problems, but can be slower for specific problems that quantum computing excels at 5. Scalability30 Quantum computing faces hardware limitations, such as qubit decoherence, which currently limits the size of practical quantum computersMachine learning scales well with the availability of more data and com- ...
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data. The Arabic language is one of the most widely spoken languages in the world, with over 420 million speakers. With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. Document classification is a fundamental task in natural language processing that involves assigning predefined categories to text documents based on their content. This task has numerous applications in various fields, including information retrieval, sentiment analysis, spam filtering, and news categorization 1. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. Quantum computing is a rapidly evolving field that leverages the principles of quantum OPEN
