The emerging quantum computing technology (QC) transforms business intelligence by creating new methods for marketing and finance to use data in their decision-making processes. This research combines a systematic literature review with a meta-analysis of 26 peer-reviewed articles spanning from 2014 to 2025 to show how quantum computing improves predictive analytics and customer segmentation and sentiment analysis, portfolio optimization, and risk modeling. The research uses PRISMA guidelines together with strategic foresight tools to detect how hybrid quantum-classical systems replace classical models by delivering exponential speed and accuracy, and scalability benefits. The research demonstrates that QSVM, QAOA, and quantum-enhanced neural networks serve as primary methodologies to create real-time, emotionally intelligent, highly adaptive decision systems. The thematic analysis, together with conceptual mapping, shows that quantum tools are converging into a unified interdisciplinary space that combines marketing with finance and supply chains and pricing strategies. The study reveals important research deficiencies in multilingual datasets as well as benchmarking and ethical readiness, and suggests a strategic integration plan for global implementation by 2030. The research demonstrates that quantum computing represents a strategic capability that enables future-ready data-driven enterprises to enter a new business intelligence era based on quantum resilience.
The emerging quantum computing technology (QC) transforms business intelligence by creating new methods for marketing and finance to use data in their decision-making processes. This research combines a systematic literature review with a meta-analysis of 26 peer-reviewed articles spanning from 2014 to 2025 to show how quantum computing improves predictive analytics and customer segmentation and sentiment analysis, portfolio optimization, and risk modeling. The research uses PRISMA guidelines together with strategic foresight tools to detect how hybrid quantum-classical systems replace classical models by delivering exponential speed and accuracy, and scalability benefits. The research demonstrates that QSVM, QAOA, and quantum-enhanced neural networks serve as primary methodologies to create real-time, emotionally intelligent, highly adaptive decision systems. The thematic analysis, together with conceptual mapping, shows that quantum tools are converging into a unified interdisciplinary space that combines marketing with finance and supply chains and pricing strategies. The study reveals important research deficiencies in multilingual datasets as well as benchmarking and ethical readiness, and suggests a strategic integration plan for global implementation by 2030. The research demonstrates that quantum computing represents a strategic capability that enables future-ready data-driven enterprises to enter a new business intelligence era based on quantum resilience.
The convergence of quantum computing and artificial intelligence represents a transformative technological paradigm with unprecedented potential for business intelligence applications. This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains. Through systematic analysis of emerging research, implementation frameworks, and interdisciplinary case studies, we investigate how quantum computing's unique computational mechanisms can fundamentally reshape data analysis, strategic decision-making, and predictive modeling. Our comprehensive examination reveals that quantum AI systems demonstrate remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains. The research synthesizes evidence from multiple technological domains, highlighting the transformative potential of quantum-enhanced AI in solving previously intractable computational problems. By exploring technological capabilities, implementation challenges, and future research directions, this review provides a critical framework for understanding the emerging intersection of quantum computing and artificial intelligence in advanced business intelligence applications.
Quantum computing is transforming consumer behavior analysis and market research, introducing a paradigm shift in predictive analytics and decision intelligence. This paper presents a theoretical framework integrating quantum computing principles with consumer decision models, addressing the complexity and uncertainty of consumer choices. By leveraging quantum probability, superposition, and entanglement, this study enhances market research methodologies, allowing businesses to analyze multi-dimensional consumer data with superior accuracy. The article introduces Quantum-Like Bayesian Networks and Markov Quantum Decision Models, providing a context-aware, probabilistic alternative to traditional predictive models. These frameworks capture behavioral inconsistencies, real-time decision shifts, and contextual influences, redefining consumer insights. Additionally, this study explores quantum sentiment analysis, quantum-enhanced predictive modeling, and real-time data processing, establishing quantum computing as a disruptive force in market intelligence. Beyond analytical advancements, the paper examines quantum cryptography for secure consumer data processing, quantum blockchain for consumer trust, and quantum-driven personalization strategies, demonstrating their potential for revolutionizing engagement and decision automation. Finally, the article addresses the ethical and operational challenges of quantum-driven consumer research, outlining future research directions and real-world applications. As quantum technology advances, it is poised to reshape predictive analytics, decision intelligence, and strategic marketing, offering businesses and researchers an unprecedented tool for next-generation market research..
Abstract Quantum computing (QC) advances will help solve high complexity challenges that classical system architectures cannot solve. This report examines the philosophical foundation for QC research using a PRISMA-compliant systematic literature review and bibliometric analysis. This review emphasizes business applications of QC, including optimization, cryptography and cybersecurity, machine learning, financial modeling and forecasting, supply chain management, healthcare and analytics, and materials design, showing how QC can improve prediction and forecast accuracy, data physicalness, large-scale optimization, and more. The research will also identify execution constraints such hardware readiness, qubit capacity, development and operation expenses, personnel availability, and regulatory and commercial governance. Bibliometric data showed a focus on foundational quantum theoretical contributions and algorithms, with multidisciplinary and practical work emerging. Scaled algorithms, quantum-resistant standards, and any solutions industry nexus to enterprise are among the research opportunities. The literature evaluation will also identify ethical, policy, and sustainability issues related to quantum benefits. The paper helps businesspeople understand QC’s current and future opportunities, including a research-based assessment of where we’ve come, common themes in the literature, and strategic domains for scaling QC as a research agenda from laboratory experiments to enriching the service experience as commercial utility.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
The integration of quantum computing into financial systems marks a transformative leap in risk management, security, and market operations. Unlike classical computing, quantum algorithms can process vast datasets and complex models at unprecedented speeds, enabling real-time risk analytics, rapid fraud detection, and optimized trading strategies. This study explores the emerging field of quantum-driven finance, focusing on its potential to enhance predictive accuracy, reduce systemic vulnerabilities, and accelerate decision-making in volatile markets. Applications such as quantum-enhanced Monte Carlo simulations, quantum machine learning for anomaly detection, and optimization in algorithmic trading are examined alongside challenges in scalability, cybersecurity, and regulatory adaptation. By evaluating current advancements and future prospects, this research highlights quantum computing's role in shaping a secure, intelligent, and high-velocity global financial ecosystem.
AI and quantum computing are increasingly transforming corporate control. This study incorporates quantum artificial intelligence (QAI) into commercial decision-making, operations, and strategic planning. Quantum algorithms' immense processing capacity and probabilistic nature can improve business analysis, modeling, and prediction. QAI may change business analytics, risk management, supply chain optimization, market forecasting, and consumer behavior analysis. Quantum-improved machine learning, neural networks, and data-driven insights help companies uncover patterns and anomalies faster. In addition, case examples and theoretical models demonstrate how quantum-based algorithms improve real-time uncertainty-based decision-making. Multidisciplinary cooperation, morality, and digital infrastructure development are all studied in QAI technology's organizational preparation and strategic ramifications. This chapter discusses how quantum computational electricity and AI-driven systems may revolutionize company management as industries prepare for quantum technology.