Big data and quantum computation

Background. Quantum computing is a technology that has great potential to revolutionize various scientific disciplines with its extraordinary computing capabilities. Multiple fields, such as cryptography, optimization, and complex simulation, have begun to explore quantum computing applications. However, a deep understanding of how these technologies can be used effectively in cross-disciplinary informatics still needs to be improved. Purpose. This research aims to develop a theoretical framework that explains the role of quantum computing in advancing interdisciplinary informatics. The main focus is to identify potential applications and challenges faced in applying this technology in various fields and evaluate its impact on the development of science and technology. Method. This research uses a qualitative approach, a literature review, and theoretical analysis methods. Data were collected from various academic sources, including journals, books, and conferences relevant to quantum computing and interdisciplinary informatics. Analysis was conducted to identify patterns, themes, and relationships between quantum computing and cross-disciplinary applications. Results. The research results show that quantum computing has the potential to solve complex computational problems in various fields, including cryptography, optimization, and physical simulation. Quantum computing can provide more efficient and faster solutions than classical computing. However, the research also identified several significant challenges, such as the need for quantum-specific algorithms and sophisticated technological infrastructure. Conclusion. Quantum computing is essential in advancing interdisciplinary informatics by providing superior computing capabilities. The theoretical framework developed in this research can be used to further study and develop quantum computing applications in various fields. The identified challenges require special attention to ensure effective and efficient implementation of this technology.

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.

Clustering bike sharing stations using Quantum Machine Learning: A case study of Toronto, Canada

Quantum machine learning [QML] is a new formulation on quantum hardware platform will try to achieve more enhanced data analysis and prediction which classical computer will not be able to generate. Classical computers are having computational limitations in terms of large volume data processing. Quantum machine are not to replace classical machines but quantum computers will solve operational difficulties of classical machines in terms of computational time. Quantum machine learning accelerates the supervised, unsupervised, and reinforcement learning methods. Classical ML methods such as SVM, PCA, Clustering, Neural networks are giving promising results but classical machines are inadequate to perform certain computations. Proposed Quantum machine learning would result in complex and weird patterns. Quantum support vector machine(QSVM) is a method used in supervised learning for classification and regression. QSVM uses high-dimensional feature space possibly on infinite dimension called as enhanced feature space for generating hyperplane. This hyperplane will classify non-linear and complex data on multiclass domain to achieve improved accuracy in less computational time. This paper investigates various strategies for quantum enhanced machine learning algorithm in supervised learning method. KeywordsQuantum computingMachine learningQubitQuantum machine learningSupport vector machine

Recent improvements in control of quantum systems make it seem feasible to finally build a quantum computer within a decade. While it has been shown that such a quantum computer can in principle solve certain small electronic structure problems and idealized model Hamiltonians, the highly relevant problem of directly solving a complex correlated material appears to require a prohibitive amount of resources. Here, we show that by using a hybrid quantum-classical algorithm that incorporates the power of a small quantum computer into a framework of classical embedding algorithms, the electronic structure of complex correlated materials can be efficiently tackled using a quantum computer. In our approach, the quantum computer solves a small effective quantum impurity problem that is self-consistently determined via a feedback loop between the quantum and classical computation. Use of a quantum computer enables much larger and more accurate simulations than with any known classical algorithm, and will allow many open questions in quantum materials to be resolved once a small quantum computer with around one hundred logical qubits becomes available.

Abstract: This book presents a comprehensive exploration of quantum cryptography as an emergent paradigm in secure communications, enhanced through the integration of quantum computing and machine learning methodologies. Grounded in the principles of quantum mechanics—superposition, entanglement, and the no-cloning theorem—it develops a conceptual and mathematical framework for information-theoretic security resistant to both classical and quantum adversaries. The work identifies the limitations of classical cryptographic systems in the advent of large-scale quantum computation and proposes hybrid architectures that leverage machine learning for adaptive threat detection, protocol optimization, and anomaly classification in quantum key distribution (QKD) environments. Methodologically, the text synthesizes theoretical analysis, formal security proofs, and empirical evaluation using both quantum simulation platforms and experimental testbeds. Key results demonstrate that quantum–machine learning synergies can improve key generation rates, enhance noise tolerance, and accelerate real-time countermeasure deployment. The implications extend to the design of scalable, interoperable cryptographic systems compatible with emerging quantum internet infrastructures, while also addressing ethical and regulatory considerations in deployment. By combining rigorous academic analysis with practical implementation insights, this book establishes a scholarly and applied foundation for next-generation secure communication systems in the quantum era. Keywords Quantum cryptography, quantum key distribution, quantum computing, machine learning, quantum machine learning, cryptographic security, post-quantum cryptography, hybrid architectures, adaptive protocol optimization, information-theoretic security, secure communications, quantum internet, ethical implications in cryptography

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.

Recently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field developed by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, the effects of superposition and entanglement on weather forecasting, were investigated using a variational quantum circuit model when the dataset size is small. The use of the entanglement layer between the variational layers has made significant improvements on the circuit performance. The use of the superposition layer before the data encoding layer resulted in the use of less variational layers.

In this serie of lectures we present some of the basic issues in semiconductor spintronics at an introductory level. We start with a short overview of low dimensional semiconductor structures and their applications. We then move on to discuss the notions of spin generation, injection, relaxation and detection in spintronics, with an emphasis in the application of diluted magnetic semiconductors for spin generation and injection. Finally, we discuss the manipulation of spin in semiconductor nanostructures like quantum dots, and its applications in quantum computation.

Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Quantum Machine Learning (QML) is emerging as a transformative technology in cybersecurity, particularly in anomaly detection for cyber security audits. Traditional machine learning models are effective but face scalability and efficiency limitations as cyber threats grow more sophisticated. QML, leveraging quantum computing’s ability to process and analyze large datasets in parallel, offers potential breakthroughs in identifying anomalous patterns that could signify cyber threats such as data breaches, insider threats, or unauthorized access. Content Analysis Research Methodology used in this research work. This paper explores the integration of QML into anomaly detection systems for cyber security audits, where detecting deviations from normal behavior is crucial. Quantum algorithms, particularly those based on Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Quantum Principal Component Analysis (QPCA) can enhance the detection of subtle anomalies that classical algorithms may overlook due to noise or the complex, high-dimensional nature of cyber data. The inherent properties of quantum computing, such as superposition and entanglement, allow for more efficient feature selection and optimization, potentially leading to faster and more accurate anomaly detection. The impact of implementing QML in cyber security audits is profound. First, it enhances detection capabilities by identifying anomalies with greater precision, reducing false positives, and improving response times to cyber incidents. Second, quantum algorithms’ ability to manage exponentially large datasets makes them ideal for environments with extensive data logs, such as enterprise networks and cloud infrastructures. Third, as cyber threats become increasingly adaptive and stealthy, QML offers a dynamic solution that evolves alongside these threats by continuously learning from new patterns of attack. However, practical challenges remain, including the need for quantum hardware advancements, the development of hybrid quantum-classical models, and ensuring the interpretability of quantum models in audit scenarios. Despite these challenges, early research and experimental implementations demonstrate the potential of QML to revolutionize anomaly detection in cybersecurity audits. This paper concludes that while QML is still in its early stages, its application to anomaly detection holds promise for significantly enhancing the effectiveness of cyber security audits. The impact of this technology, when fully realized, could redefine how organizations protect their networks and data from ever-evolving cyber threats, making QML a critical area for future research and development in cybersecurity.

In the world of computation, the invention of Computers has reduced human effort for accomplishing task. Depending on the type, size and nature of the work the computers are manufactured in different magnitudes. As the technology grows, it facilitated to manufacture high performing, high throughput, and high memory Classical computers. Classical computers store any information in the form of bits in the memory device, which can be either 1 or 0 (binary system) and all the processing will be done in the same logic i.e., high voltage/low voltage. Because of this logic the classical computers solve some problems by using more resources. Quantum computer are the next level computers which uses quantum theory principles for solving problems. The quantum computer uses Qubit as basic unit which uses principles of quantum computing such as superposition and entanglement, which gives qubit, the efficiency of exhibiting many logical states at a time. This paper shows a comparative study on classical and quantum computers and illustrates various problems in classical computers and respective solutions with use of quantum computers.

This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.