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.
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.
Quantum economics is an interdisciplinary field that combines the principles of quantum mechanics and economic theories. The research examines the main theoretical and practical aspects of quantum economics, starting with its theoretical foundations based on the principles of quantum mechanics, such as superposition, uncertainty and entanglement, and ending with practical applications and perspectives for further research. Special attention was paid to the potential impact of quantum technologies on finance, marketing, supply chain management, as well as on macroeconomic planning and policy. It discusses how quantum algorithms can transform traditional approaches in economic research and practice, providing new methods for analyzing complex data, improving forecasting of economic trends, and optimizing decisions under uncertainty. Quantum economics opens up new perspectives for understanding dynamic and interconnected processes in the global economy, which can fundamentally change our perception of economic laws and strategies. It is emphasized that the quantum economy is at the initial stage of its development, and scientists and practitioners face many challenges and questions that need to be solved. In the future, we can expect the development of new tools for economic analysis and decisionmaking based on quantum computing, which will allow us to cope with current and future economic challenges more effectively. The quantum economy can also offer new approaches to risk management and financial security, especially in light of growing cyber threats and the need to protect economic information. Quantum economics is an exciting and promising direction that can significantly enrich both theoretical and applied aspects of economic science.
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.
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.
In recent years, quantum economics has emerged as a revolutionary discipline that applies the fundamental principles of quantum physics to offer a new perspective on the uncertainty and complexity inherent in modern economic systems. Quantum cognition plays a fundamental role in this framework, explaining how individuals process information and make decisions in contexts of ambiguity and uncertainty. Using quantum mechanisms such as the superposition of mental states, interference, and entanglement, quantum cognition models complex behavioral phenomena that are inexplicable by classical decision theory, such as the effects of presentation order and cognitive paradoxes. This article examines the intersection between quantum economics, quantum cognition, decision-making, and sustainability in financial environments through a systematic review following the PRISMA methodology and a bibliometric analysis. Recent advances in artificial intelligence and quantum computing are driving the practical application of these approaches, improving the management of uncertainty in sectors such as renewable energy and sustainable resource management. The results show that quantum economics is a promising framework for the design of policies and strategies aimed at achieving the Sustainable Development Goals (SDGs).
Quantum economics postulates ideas about quantum money, quantum finance, and quantum social science and shows that the economy is an archetypal example of a quantum social system, complete with its versions of measurement, i.e., uncertainty, entanglement, superposition, and so on.A sizeable literature on quantum economics has grown in recent periods.This paper reviews some theoretical developments and empirical studies on Quantum economics to provide a comprehensive analysis of the potential implications of Quantum Physics and its philosophical ideas in the domains of economics, finance, and social science.Moreover, this studies also focused Why the field of Quantum Economics arises" how Quantum economics links physics, economics, and social science.All reviews and research papers examine the use of learning strategies in Quantum Economics.The overall results shows the following Possible applications of quantum Physics' in Economics: (i) enhanced computational capabilities for big data and complex problems, (ii) secure quantum-based currency, (iii) innovative tools for policymaking, and (iv) advanced behavioral and decisionmaking analysis under uncertainty.This paper provides an overview of the Quantum economics literature, background history, conceptual insights, economic policy, and conceptual and methodological critique.Finally, we present an agenda for future research in the field.
This chapter introduces the revolutionary potential of quantum computing in finance, referencing its promise to revolutionize risk management, portfolio optimization, market forecasting, and data protection. It describes key quantum algorithms such as Grover's and Shor's, and technologies such as quantum annealing and quantum machine learning, while illustrating how the advances dramatically decrease computational complexity and improve decision-making efficiency. The research also includes the limitations of classical financial models and describes how quantum approaches bridge these limitations via superior stochastic modelling, real-time rebalancing, and high-level cryptographic systems. The paper also addresses challenges to implementation, including hardware constraints, high implementation costs, regulatory complexity, and the need for immediate interdisciplinary expertise. The paper lastly prescribes strategic imperatives for financial institutions, policymakers, and investors in preparation for an impending future enabled by quantum technology.