The convergence of quantum computing and economics represents a frontier in computational social science, promising to transform the modeling of uncertainty, complexity, and interdependence in financial and macroeconomic systems. This study explores how quantum computing methodologies—particularly quantum annealing, variational algorithms, and hybrid quantum–classical frameworks—can be integrated into Dynamic Stochastic General Equilibrium (DSGE) modeling, stochastic simulations, and systemic risk analytics. By focusing on collaborative university–industry partnerships, the paper examines mechanisms that accelerate translational research, facilitate scalable experimentation, and bridge theoretical quantum advances with applied financial modeling. The analysis highlights how quantum-enhanced algorithms can improve the tractability and precision of multidimensional optimization problems inherent in macroeconomic forecasting and risk estimation. It also emphasizes the institutional value of joint academic–industrial infrastructures in fostering data sharing, interdisciplinary education, and early adoption of post-classical computing in finance. The findings suggest that hybridized quantum frameworks can substantially advance decision sciences, enabling resilient and adaptive economic systems capable of responding to global shocks with greater computational foresight.
The convergence of quantum computing and economics represents a frontier in computational social science, promising to transform the modeling of uncertainty, complexity, and interdependence in financial and macroeconomic systems. This study explores how quantum computing methodologies—particularly quantum annealing, variational algorithms, and hybrid quantum–classical frameworks—can be integrated into Dynamic Stochastic General Equilibrium (DSGE) modeling, stochastic simulations, and systemic risk analytics. By focusing on collaborative university–industry partnerships, the paper examines mechanisms that accelerate translational research, facilitate scalable experimentation, and bridge theoretical quantum advances with applied financial modeling. The analysis highlights how quantum-enhanced algorithms can improve the tractability and precision of multidimensional optimization problems inherent in macroeconomic forecasting and risk estimation. It also emphasizes the institutional value of joint academic–industrial infrastructures in fostering data sharing, interdisciplinary education, and early adoption of post-classical computing in finance. The findings suggest that hybridized quantum frameworks can substantially advance decision sciences, enabling resilient and adaptive economic systems capable of responding to global shocks with greater computational foresight.
Quantum computing has emerged as a revolutionary computational paradigm capable of solving complex optimization and probabilistic problems that are computationally expensive for classical computers. The financial industry, characterized by large-scale datasets, uncertainty, stochastic behavior, and multidimensional optimization challenges, is increasingly exploring quantum computing to enhance decision-making processes. This paper presents a comprehensive investigation into the applications of quantum computing in financial modeling, emphasizing portfolio optimization, derivative pricing, risk assessment, fraud detection, and algorithmic trading. Unlike conventional computational techniques that suffer from exponential complexity when processing high-dimensional financial data, quantum algorithms leverage superposition, entanglement, and quantum parallelism to improve computational efficiency and solution quality. The study discusses fundamental quantum computing concepts, analyzes existing financial modeling frameworks, and examines how quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Monte Carlo (QMC), Grover’s Search Algorithm, and Quantum Machine Learning (QML) contribute to solving real-world financial problems. Furthermore, the paper reviews recent research developments, identifies implementation challenges associated with noisy intermediate-scale quantum (NISQ) devices, and highlights future opportunities for hybrid quantum-classical financial systems. The findings demonstrate that quantum computing has significant potential to transform computational finance by improving prediction accuracy, accelerating optimization tasks, and enabling scalable financial analytics for next-generation intelligent financial ecosystems.
Quantum computing has emerged as a transformative computational paradigm, offering novel capabilities to tackle complex problems in finance—most notably in derivative pricing, risk management, and portfolio optimization. This review synthesizes recent advances in quantum algorithms and hybrid quantum–classical frameworks as applied to financial services, examining how technologies such as quantum-annealing, quantum amplitude estimation, and variational quantum circuits can improve efficiency, accuracy, and scalability over classical methods. The study provides a systematic assessment of key use-cases: (1) derivative pricing and greeks computation via quantum Monte Carlo and amplitude estimation; (2) risk management enhancements, including Value-at-Risk (VaR) and conditional VaR through quantum gradient estimation; and (3) portfolio optimization via QUBO (quadratic unconstrained binary optimization) mapping, the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing approaches. In addition to reviewing technological potential, the paper addresses critical barriers—hardware noise, coherence limitations, data integration, regulatory considerations—and outlines future research directions, including quantum-resilient cryptography in finance and industry coupling. By bringing together algorithmic, financial and regulatory perspectives, this review seeks to guide academics and practitioners toward the next frontier in financial computing.
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
Quantum computing represents one of the most disruptive technological paradigms of the 21st century, with profound implications for economic modelling, financial optimization, and computational decision-making. Unlike classical computing, quantum systems leverage superposition, entanglement, and interference to solve problems of exponentially higher complexity, a capability with transformative potential for economics and finance. This study examines the intersection of quantum computing and economics, systematically evaluating both the promises and the barriers defining this emerging field. Regarding opportunities, quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, demonstrate significant theoretical advantages in portfolio optimization, risk assessment, option pricing, and the resolution of large-scale combinatorial problems that are intractable for classical machines. Quantum machine learning extends these prospects, offering accelerated capabilities for pattern recognition and predictive modelling relevant to financial markets. However, substantial barriers temper these expectations. Current quantum hardware operates in the NISQ (Noisy Intermediate-Scale Quantum) era, where decoherence, gate errors, and a limited number of qubits constrain practical applicability. The gap between theoretical quantum advantage and real-world economic implementation remains significant, compounded by a shortage of specialists, high infrastructure costs, and the absence of standardized programming frameworks tailored to economic applications. Through a bibliometric analysis of existing literature and a critical review of recent experimental results, the study maps the current state of quantum computing adoption in economics, identifies the most active research frontiers—quantum game theory and quantum portfolio optimization—and outlines the conditions under which quantum advantage could become economically relevant. The findings suggest that, although quantum computing will not transform economic practice in the short term, its long-term disruptive potential warrants proactive engagement from economists, financial institutions, and policymakers.
This chapter maps the technological convergences that are reshaping quantum finance through AI, blockchain technology, and quantum computing. It discusses how these innovations enable the practical implementation of quantum financial principles at an unprecedented scale. The analysis reveals how AI enables quantum-like decision-making through parallel scenario processing, pattern recognition in complex data, and adaptive trading algorithms that respond to market uncertainty in real time. Blockchain applications, including DeFi protocols, smart contracts, and central bank digital currencies (CBDCs), create quantum-like financial infrastructure that operates across multiple states simultaneously. Quantum computing applications focus on optimization problems that classical computers cannot solve, enabling true portfolio superposition and risk calculation across exponential scenario sets. The chapter examines three critical convergence areas: quantum-inspired trading algorithms deployed at JPMorgan Chase and Goldman Sachs showing practical applications; collaborative frameworks between financial institutions, technology companies, behavioral scientists, and regulators necessary for quantum finance implementation; and educational infrastructure development, including university-industry partnerships and professional training programs. Advanced applications explore quantum approaches to sustainable investing (ESG) integration and holistic decision-making models that blend technological capability with human insight. The analysis maintains the book’s core focus on embracing uncertainty and dynamic decision-making while showing how emerging technologies make quantum finance principles practically implementable in modern financial systems.
This article explores the interdisciplinary interface between quantum computing and economic theory, with a particular emphasis on how foundational concepts from quantum mechanics reshape core methodological and epistemological assumptions in economics. Drawing on a comprehensive bibliographic methodology, the study synthesizes contributions from quantum physics, computational theory, and economic modeling to trace the impact of quantum logic -especially principles such as superposition, entanglement, and measurement irreversibility- on both theoretical constructs and applied frameworks. Particular attention is given to emblematic algorithms (e.g., Shor’s and Grover’s) as case studies for illustrating computational discontinuities in fields such as cryptographic trust, portfolio optimization, and decision-making under uncertainty. The article also surveys recent developments in quantum machine learning and quantum-inspired models relevant to economic forecasting and adaptive policy design. Ethical and distributional concerns are critically addressed, especially in relation to the asymmetric global access to quantum resources. By mapping this emerging field through a structured review of cross-disciplinary literature, the paper offers a conceptual framework for understanding how quantum technologies may influence future research in financial economics, risk theory, and the modeling of complex systems operating under deep informational constraints.