Quantum-Enabled Convergence of Big Data, Artificial Intelligence, and Financial Analytics: Theoretical Foundations, Practical Pathways, and Forward-Looking Implications

Abstract

Background: The accelerating confluence of quantum computing, big data infrastructures, and advanced artificial intelligence (AI) models is reshaping the theoretical and practical contours of financial analytics, digital marketing, supply chain optimization, and educational infrastructures. Scholarly and practitioner literatures document significant advances across each domain — quantum architectures and algorithmic paradigms, expansive big data platforms and measurement regimes, and powerful AI systems including large language models and multimodal foundation models — yet integrated frameworks that articulate the combined potential and attendant challenges remain nascent (Preskill, 2023; Tosi et al., 2024; Hong et al., 2024).

Objectives: This article synthesizes the extant literature to construct a conceptual and methodological scaffold for integrating quantum-enabled computation with big data and AI in financial and economic analysis. It clarifies problem spaces, articulates methodological choices conducive to responsible adoption, analyzes potential transformative outcomes across organizational practice and education, and identifies unresolved research gaps demanding priority attention. The approach emphasizes theoretical rigor, cross-disciplinary citation, and practical interpretive analysis to serve both academic researchers and senior practitioners.

Methods: We employ a critical integrative review method that draws on systematic literature syntheses, cross-disciplinary theoretical elaboration, and analytic argumentation. The methodology combines thematic synthesis of provided references, translation of technical claims into descriptive analytical narratives, and normative discussion of implementation pathways supported by cited evidence (Tosi et al., 2024; Sardi et al., 2023).

Findings: Quantum computing promises algorithmic speedups for classes of problems relevant to finance (optimization, sampling, and certain linear-algebraic subroutines), yet practical advantage depends on hybrid classical-quantum architectures and careful encoding of big data into quantum-accessible representations (Preskill, 2023; Rayhan & Shahana, 2023). AI advances, particularly in foundation models and multimodal systems, provide powerful representational tools that reshape feature engineering, natural language understanding, and scenario simulation for economic forecasting and M&A diligence (Arachchige et al., 2023; Hong et al., 2024). Big data infrastructures have matured over fifteen years, yet integration challenges — governance, measurement, and sustainable performance — persist and condition how quantum and AI systems can actually be deployed (Tosi et al., 2024; Sardi et al., 2023; Rashid et al., 2024).

Conclusions: The transformative potential at the intersection of quantum, big data, and AI is real but contingent. Realizing benefits in finance and related domains requires: (1) methodological advances in encoding and hybrid computation, (2) governance frameworks for data quality and ethical AI, (3) workforce re-skilling oriented to new analyst roles, and (4) empirical research focusing on scaled field deployments and measurable outcomes. We conclude with a detailed research agenda and policy recommendations designed to accelerate responsible innovation while managing risks