Quantum machine learning: revolutionizing artificial intelligence and big data analytics

Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, execution time, and noise tolerance. The study examined three leading quantum machine learning (QML) frameworks PennyLane (v0.24), Qiskit (v0.43.2), and TensorFlow Quantum (TFQ, v0.7) each tested on a 64-bit Ubuntu Linux system with an Intel Core i7 processor, 32 GB RAM, and NVIDIA GeForce RTX 3080 GPU. Quantum algorithms like variational quantum circuits (VQCs) and quantum support vector machines (QSVMs) were simulated using these frameworks. Various quantum gates, including Pauli-X, Y, Z, Hadamard, CNOT, and Rotation gates (Rx, Ry, Rz), were used for parameter optimization and quantum superposition. The Iris and MNIST datasets were adapted for quantum encoding using Amplitude and Angle Encoding for binary classification tasks. Simulations were run on classical computers, while select circuits were executed on IBM Quantum’s Falcon r5.11 processor to compare hardware performance. PennyLane achieved the highest accuracy (92%) in quantum simulations, excelling in hybrid quantum-classical model integration. TensorFlow Quantum provided the fastest execution time, especially in classical simulations, making it suitable for rapid prototyping. Qiskit produced deeper quantum circuits but had higher error rates (6%) on real quantum hardware, highlighting noise and decoherence effects. The VQC models optimized by stochastic gradient descent (SGD) showed significant improvements in model performance, and QSVMs effectively classified both linearly separable and non-separable datasets, demonstrating the potential of quantum algorithms over classical approaches. Statistical analysis confirmed the significance of the results (p < 0.05). PennyLane is the most robust framework for hybrid quantum-classical models, while TensorFlow Quantum is suited for rapid prototyping. Qiskit, despite longer execution times, excels in hardware implementation. The choice of framework should align with the specific needs of quantum machine learning tasks.

Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so on. In the existing world, QML can be a game-changer for organizations that work on identifying product correlations and customer behaviour in a brief time. This paper discusses studies related to principles of Quantum Mechanics, Quantum Computing and QML algorithm. Additionally, it explores opportunities and challenges faced in the field of QML. Lastly, the paper talks about QML applications and compares various available QML toolkits from the market. Future direction and exhaustive survey of papers from various spectrums related to Quantum Computing is presented in this paper.

Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment. To our knowledge, this is the first implementation of a stabiliser-based error detection code to a QML algorithm. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without error detection. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight that extends to QML implemented with full error-correction: when the QEC code requires ancilla qubits for logical rotations but cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is constrained. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.

In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing.

Recent advances in quantum computing and machine learning have brought about a promising intersection of these two fields, leading to the emergence of quantum machine learning (QML). However, the integration of quantum computing and machine learning poses several challenges. One of the prominent challenges lies in the presence of barren plateaus (BP) in QML algorithms, particularly in quantum neural networks (QNNs). Recent studies have successfully identified the fundamental causes underlying the existence of BP in QNNs. This paper presents a framework designed to explore the interplay of multiple factors contributing to the BP problem in quantum neural networks (QNNs), which poses a critical challenge for the practical applications of QML. We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. Our framework aims to empirically analyze the joint impact of these factors on the training landscape of HQNNs. Our results show that the occurrence of the BP problem in HQNNs is contingent upon the expressibility of the underlying ansatz and the type of the adopted data encoding technique. Additionally, we observe that qubit entanglement also plays a role in exacerbating the BP problem. Leveraging various evaluation metrics for classification tasks, we systematically evaluate the performance of HQNNs and provide recommendations tailored to different constraint scenarios. Our findings emphasize the significance of our framework in addressing the practical success of QNNs.

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

Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. The proposed methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling (Twir), and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.

Coronavirus Diseases (COVID-19) is an infectious disease caused by a newly discovered coronavirus that becomes world pandemic with 200 countries recorded affected, and nearly 1 million people died. Starting from Wuhan in December 2019, within three months, the spread across global with high reproduction rates (R Rates). There is evidence in one case, it spread to more than 100 people and creates his pandemic cluster. As the pandemic contributes to a large volume of data, Artificial Intelligence (AI) and Big Data Analytics (BDA) play a huge role in understanding the pan-demic to help necessary action can be deployed. Researchers and developers are increasingly using artificial intelligence, machine learning, and natural language processing to track and contain coronavirus and gain a more comprehensive understanding of the disease. So far, due to new diseases, there is a limited study to cover how AI and BDA will help in fighting COVID-19. Therefore, we provide a comprehensive analysis of the existing and potential of using AI and BDA to manage the COVID-19 outbreak based on COVID-19 Outbreak Life Cycle phases; detection, spread, management, recovery. We also presented the challenges needed to be overcome for BI in BDA in the fighting. To conclude, these findings show the necessity of AI and BDA as a critical tool to understand COVID-19 and there a lot of ongoing intensive works have been carried out to cope with COVID-19.KeywordsCOVID-9Artificial intelligenceBig Data AnalyticsMachine learning

In a world of swift digital transformations, the global market is seeing notable structural shifts. This applies not only to novel technologies, but also to how brands engage with their consumers. Artificial intelligence (AI) and big data analytics are at the heart of this change. With these instruments, companies can analyze consumer behavior more profoundly and precisely, considering the cultural and behavioral traits of diverse audiences worldwide. This allows them to hone their marketing strategies and cultivate a strong emotional bond between the consumer and the brand. The article aims to identify and examine the opportunities that artificial intelligence and big data technologies offer to boost the efficacy of brand communication within the international market. The article reviews theoretical approaches to constructing brand communication in the global market, considering digital transformation, specifically concentrating on the concept and function of brand communication. The key models of brand communication are assessed. The potential of employing artificial intelligence to develop and manage brand communications is examined. The role of big data in making communication decisions, encompassing audience segmentation, analysis of consumer behavior, and content adjustment to cultural specificities is identified. Particular attention is given to the integration of AI and Big Data into a brand’s global communication plan. The pros and cons associated with incorporating AI and Big Data into brand communication strategies, including ethical and legal aspects, are outlined. The article suggests constructing brand communication centered on artificial intelligence and big data, and offers suggestions for brands on the effective application of digital technologies in communicating with consumers on a worldwide scale. Via artificial intelligence and data analytics, brands can ensure a consistent customer experience throughout all stages of interaction. This starts with social media and expands all the way to physical points of sale, which in turn helps to boost customer loyalty and satisfaction. Analytics of behavioral, demographic, and contextual data allows for precise segmentation, recognizing consumer needs, and predicting market trends. This produces the conditions for brands to more effectively tailor content to the cultural and regional attributes of their audience.

Advancing total quality management in higher education through AI and big data analytics

The evolution of cyberattacks requires a continuous race to implement increasingly sophisticated techniques that allow us to stay ahead of cybercriminals. Thus, a relevant inverse problem in cybersecurity involves determining underlying patterns or models of possible cyber threats based on observed data. In particular, the processing of massive data combined with the application of Machine Learning methods and other techniques derived from Artificial Intelligence have so far achieved very significant advances in preventing and mitigating the impact of many cyberattacks. Given that the keyword in cybersecurity is anticipation, this work explores the possibilities of quantum computing and, in particular, of Quantum Machine Learning to have, when the quantum computing era arrives, the most optimal parameterisations to put these models into practice. Although the application of quantum technologies in a real context may still seem distant, having studies to assess the future viability of Quantum Machine Learning to identify different types of cyberattacks may be a differential element when it comes to ensuring the cybersecurity of essential services. For this reason, this work aims to use several datasets of known problems in the field of cybersecurity to evaluate the most optimal parameterisations in some known Quantum Machine Learning models, comparing the results with those obtained using classical models. After analysing the results of this study, it can be concluded that Quantum Machine Learning techniques are promising in the context of cybersecurity, giving rise to future work on a wider range of cybersecurity datasets and Quantum Machine Learning algorithms.

This study examines the impact of artificial intelligence (AI) and big data analytics (BDA) on financial reporting quality (FRQ) in Nigeria, with particular emphasis on the mediating role of transparency. The increasing adoption of digital technologies in accounting has transformed how financial information is generated, processed, and disclosed. However, empirical evidence on how these technologies influence reporting quality in emerging economies remains limited. Drawing on Information Asymmetry Theory and the Technology–Organization–Environment (TOE) framework, this study investigates whether AI adoption and big data analytics capability enhance financial reporting quality directly and indirectly through transparency. A quantitative research design was employed using survey data collected from 312 accounting and finance professionals across public and private sector organizations in Nigeria. Structural Equation Modeling (PLS-SEM) was used to test the hypotheses. The results indicate that artificial intelligence and big data analytics significantly improve financial reporting quality, while transparency plays a partial mediating role. The findings contribute to the literature on digital transformation in accounting and provide practical insights for regulators and organizations in emerging economies.

Yapay zekâ ve büyük veri analitiği, 21. yüzyılın bilgi odaklı dünyasında yalnızca teknoloji alanında değil aynı zamanda eğitim, sağlık, kamu yönetimi ve bilgi hizmetleri gibi birçok farklı alanda da dönüştürücü etkiler yaratmaktadır. Burak Savaş Sarıçoban tarafından kaleme alınan ‘Kütüphanelerin Geleceği: Yapay Zekâ ve Büyük Verinin Kütüphanelerde Kullanımı’ başlıklı kitap, bu teknolojik dönüşümün kütüphaneler bağlamındaki yansımalarını hem kuramsal hem de uygulamalı düzeyde ele alan bir çalışmadır. Yazarın yüksek lisans tezine dayanan bu eser, yapay zekâ ve büyük veri analitiğinin kütüphaneler üzerindeki etkilerini inceleyerek, dijitalleşme sürecinde yapay zekânın en etkili teknolojilerden biri olduğu savını temel almaktadır. Kitapta, kütüphanelerin bu dönüşüm sürecinde nasıl bir yol izleyebileceği literatür taraması, saha araştırması ve uluslararası örneklerle desteklenerek ayrıntılı bir biçimde tartışılmakta ve alana özgün bir katkı sunmaktadır.

The advancements in technology over the past quarter-century have yielded significant transformations in supply chain management, alongside several other sectors. The utilization of Artificial Intelligence (AI) and Big Data Analytics (BDA) has the capacity to greatly enhance the resilience of supply networks and enable more efficient management of resources within the supply chain. The advancements in AI and BDA have brought about significant transformations in the operational practices related to supply chain resilience, making them the forefront of these innovations. It is vital to elucidate the correlation between AI, BDA and supply chain resilience in order to comprehend the manner in which these transformations manifest themselves within this domain. The integration of AI and BDA technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. The objective of this study is to explore the benefits of AI and BDA in boosting supply chain resilience and visibility many. The researchers employed the Kitchenham guidelines to conduct a systematic literature review. The investigation is carried out utilizing online scholarly database systems, yielding a total of 207 initial publications, which are further narrowed down to 30 published studies spanning the period from 2013 to 2023. The research paper could be of interest to pertinent players in the business sector due to its provision of a comprehensive examination of the potential benefits associated with AI and BDA in the context of supply chain resilience and visibility.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.