The remarkable expansion of e-commerce, together with its competitive market, has created difficulties for traditional machine learning models to forecast customer actions when dealing with intricate datasets. The encountered problems with conventional machine learning models in predictive analytics for customer behaviour in e-commerce have led researchers to explore quantum computing algorithms as a potential solution. The main goal of this research work is to analyse how quantum computing technology and quantum algorithms can address the problems of inaccuracy and high dimensionality and speed and unbalanced datasets. The system implementation integrates quantum infrastructure through quantum processing units that utilize parallel processing to handle large datasets efficiently. The proposed algorithms for customer behaviour prediction models include Quantum Support Vector Machines, Quantum K-means Clustering, Quantum Boltzmann Machines, Quantum Principal Component Analysis, Quantum Neural Networks, and Quantum Ensemble Learning. These algorithms use quantum parallelism to achieve better accuracy while speeding up computations and handling unbalanced datasets. The AI framework combines traditional machine learning pipeline with quantum computing enhancements to implement quantum algorithms. The framework consists of data pre-processing followed by Quantum Machine Learning (QML) model training and Quantum-Powered AI to address imbalanced data while reducing prediction biases. Real-time insights and visualization and analytics from the advance predictive analytics dashboard enable stakeholders to make data-driven decisions. The system implements security and privacy measures through quantum cryptography which uses quantum cryptographic algorithms to boost customer data security. The research results present a potential solution to predictive analytics challenges which would transform the E-commerce industry.
The remarkable expansion of e-commerce, together with its competitive market, has created difficulties for traditional machine learning models to forecast customer actions when dealing with intricate datasets. The encountered problems with conventional machine learning models in predictive analytics for customer behaviour in e-commerce have led researchers to explore quantum computing algorithms as a potential solution. The main goal of this research work is to analyse how quantum computing technology and quantum algorithms can address the problems of inaccuracy and high dimensionality and speed and unbalanced datasets. The system implementation integrates quantum infrastructure through quantum processing units that utilize parallel processing to handle large datasets efficiently. The proposed algorithms for customer behaviour prediction models include Quantum Support Vector Machines, Quantum K-means Clustering, Quantum Boltzmann Machines, Quantum Principal Component Analysis, Quantum Neural Networks, and Quantum Ensemble Learning. These algorithms use quantum parallelism to achieve better accuracy while speeding up computations and handling unbalanced datasets. The AI framework combines traditional machine learning pipeline with quantum computing enhancements to implement quantum algorithms. The framework consists of data pre-processing followed by Quantum Machine Learning (QML) model training and Quantum-Powered AI to address imbalanced data while reducing prediction biases. Real-time insights and visualization and analytics from the advance predictive analytics dashboard enable stakeholders to make data-driven decisions. The system implements security and privacy measures through quantum cryptography which uses quantum cryptographic algorithms to boost customer data security. The research results present a potential solution to predictive analytics challenges which would transform the E-commerce industry.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.
Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.
In the era of data-driven decision-making, the exponential growth of data has posed unprecedented challenges for traditional computing methods. Big Data, characterized by vast volumes, high velocity, and diverse formats, demands innovative approaches to processing and analytics. Quantum computing, a disruptive technology harnessing the principles of quantum mechanics, offers a promising solution to tackle these challenges.This research provides a concise overview of the potential of quantum computing in the context of Big Data processing and analytics. We explore how quantum computing's unique properties, such as superposition and entanglement, can revolutionize data handling and analysis. Quantum algorithms, like Grover's and Shor's algorithms, are introduced as powerful tools for searching and factoring large datasets, respectively, with remarkable speedups compared to classical counterparts. Quantum machine learning algorithms, such as quantum support vector machines and quantum neural networks, demonstrate the potential to unlock insights from vast datasets more efficiently than classical counterparts. Quantum computing also promises to enhance data security through quantum-resistant encryption schemes, addressing the vulnerabilities of classical cryptography.
Artificial intelligence has become a transformative technology for predictive modelling across scientific, industrial, financial, and healthcare applications. However, the increasing complexity of large-scale datasets, high-dimensional feature spaces, and computationally intensive learning algorithms has exposed significant limitations in conventional computing architectures. Recent developments in quantum computing and high-performance computing have created unprecedented opportunities to accelerate artificial intelligence by improving computational efficiency, optimization capability, and predictive accuracy. This paper presents a comprehensive study of a quantum-driven artificial intelligence framework that integrates quantum algorithms with high-performance computing infrastructure to enhance predictive modelling performance. The proposed framework combines quantum-enhanced optimization, parallel heterogeneous computing, distributed data processing, and hybrid machine learning architectures to address computational bottlenecks encountered in conventional predictive systems. The study reviews recent developments in quantum artificial intelligence, evaluates emerging computational paradigms, identifies current research challenges, and proposes a scalable architecture suitable for future intelligent decision-support systems. The findings demonstrate that integrating quantum computation with high-performance computing can significantly improve prediction quality, optimization efficiency, scalability, and computational sustainability across diverse application domains while establishing a foundation for next-generation intelligent computing environments.
Advancements in quantum computing hold the potential to revolutionize artificial intelligence (AI), particularly in the field of healthcare. This paper explores how quantum computing can be leveraged to improve predictive analytics and facilitate personalized medicine. Through enhanced computational capacity, quantum computing enables faster processing and analysis of large, complex datasets, essential for predictive models in healthcare. This integration can lead to more precise diagnostics, treatment options, and disease prevention strategies by refining AI’s capability to handle vast data. Quantum algorithms such as quantum neural networks and quantum support vector machines can significantly optimize machine learning models, reducing the time required for data processing and improving predictive accuracy. Personalized medicine, an emerging trend in healthcare, relies heavily on detailed patient data to tailor treatments. The combination of quantum computing and AI promises to make this process faster, more accurate, and scalable. This study presents a detailed review of the current state of quantum AI in healthcare, highlights methodologies for data analysis, and showcases a comprehensive architecture for integrating quantum computing in AI healthcare systems. We analyse relevant data from case studies to demonstrate the advantages and challenges of adopting quantum computing for AI-driven healthcare applications.
Neuromarketing combines neuroscience and traditional marketing to uncover the emotional and cognitive drivers behind consumer behavior. As retail and e-commerce sectors increasingly rely on customer data for personalized experiences, the complexity of large datasets often surpasses classical machine learning capabilities. This gap allows Quantum Machine Learning (QML) to emerge as a transformative solution, enhancing predictive power and efficiency in neuromarketing. QML improves data processing speed and pattern recognition, enabling companies to gain deeper insights into consumer preferences and engage customers more personally. This chapter evaluates QML's applications in retail and e-commerce, highlighting how quantum algorithms can overcome current AI limitations regarding speed and precision. It also addresses data privacy concerns, emphasizing ethical data handling practices to foster consumer trust. Ultimately, the chapter envisions QML as a pivotal technology that will redefine consumer engagement and optimize marketing strategies in an increasingly data-driven landscape.