Introduction. Modern financial science increasingly faces a paradox: classical models based on deterministic assumptions fail to adequately describe the nonlinearity and uncertainty of public decision-making. In this context, the quantum paradigm of thinking, originally developed in physics, is becoming increasingly relevant as it gradually penetrates the fields of economics and finance. Problem Statement. In contemporary academic discourse, growing attention is being paid to the integration of the principles of quantum logic into socio-economic systems. Traditional financial science rests upon classical macroeconomic determinants that assume linearity, causal determinacy, and parameter stability. However, in the modern world – characterized by high uncertainty, dynamic crises, and global financial fluctuations – such logic proves insufficient. Therefore, there is a pressing need to employ quantum logic as a modern cognitive and analytical paradigm that enables the comprehension of the essence and structure of public finance through the prism of nonlinear, stochastic, and interrelated phenomena and processes. Purpose is to establish a theoretical and methodological foundation for applying quantum principles in the domain of public finance and to identify directions for their practical implementation. Methods. To obtain the scientific results presented in the article, the following methods were employed: methods of quantum logic, synthesis and analysis, deduction and induction, an abstract-logical method of “negation of negations” in financial decision-making, and theoretical generalization to define the nature of quantum finance. Results. The article reveals the potential for interpreting key principles of quantum physics within the system of public finance. A dual-level research framework is proposed, combining methodological analogies between quantum and financial processes with the practical application of quantum models in state financial management. It is demonstrated that the concepts of uncertainty, superposition, and coherence allow for a deeper understanding of the nature of budgetary decisions, tax expectations, debt imbalances, and behavioral effects in the public sphere. Conclusions. Quantum logic crystallizes a methodological framework for modern public finance capable of accounting for the complexity, uncertainty, and interconnectivity of global transformations, hybrid wars, and the contemporary economy. The application of quantum paradigms makes it possible to construct dynamic, probabilistic models of financial policy that significantly surpass the capacities of classical deterministic methods and open new perspectives for scientifically grounded public governance in the field of public finance.
Introduction. Modern financial science increasingly faces a paradox: classical models based on deterministic assumptions fail to adequately describe the nonlinearity and uncertainty of public decision-making. In this context, the quantum paradigm of thinking, originally developed in physics, is becoming increasingly relevant as it gradually penetrates the fields of economics and finance. Problem Statement. In contemporary academic discourse, growing attention is being paid to the integration of the principles of quantum logic into socio-economic systems. Traditional financial science rests upon classical macroeconomic determinants that assume linearity, causal determinacy, and parameter stability. However, in the modern world – characterized by high uncertainty, dynamic crises, and global financial fluctuations – such logic proves insufficient. Therefore, there is a pressing need to employ quantum logic as a modern cognitive and analytical paradigm that enables the comprehension of the essence and structure of public finance through the prism of nonlinear, stochastic, and interrelated phenomena and processes. Purpose is to establish a theoretical and methodological foundation for applying quantum principles in the domain of public finance and to identify directions for their practical implementation. Methods. To obtain the scientific results presented in the article, the following methods were employed: methods of quantum logic, synthesis and analysis, deduction and induction, an abstract-logical method of “negation of negations” in financial decision-making, and theoretical generalization to define the nature of quantum finance. Results. The article reveals the potential for interpreting key principles of quantum physics within the system of public finance. A dual-level research framework is proposed, combining methodological analogies between quantum and financial processes with the practical application of quantum models in state financial management. It is demonstrated that the concepts of uncertainty, superposition, and coherence allow for a deeper understanding of the nature of budgetary decisions, tax expectations, debt imbalances, and behavioral effects in the public sphere. Conclusions. Quantum logic crystallizes a methodological framework for modern public finance capable of accounting for the complexity, uncertainty, and interconnectivity of global transformations, hybrid wars, and the contemporary economy. The application of quantum paradigms makes it possible to construct dynamic, probabilistic models of financial policy that significantly surpass the capacities of classical deterministic methods and open new perspectives for scientifically grounded public governance in the field of public finance.
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.
This chapter introduces quantum-inspired thinking in finance for professionals seeking to move beyond static market analysis. It demonstrates how financial systems naturally mirror quantum traits such as uncertainty and interconnectedness through accessible, nontechnical explanations. Key concepts include superposition (holding multiple outcomes simultaneously), wave-function collapse (how measurement shapes results), and entanglement (instantaneous market interconnections). The chapter covers three main areas: the inherent quantum nature of finance and why traditional static metrics fail, specific limitations of classical financial models, and practical quantum concepts illustrated through real-world examples. A comprehensive historical perspective traces quantum signatures through major financial crises. The chapter advocates embracing uncertainty as finance’s core characteristic while maintaining existing accounting structures, setting the foundation for adaptive decision-making frameworks.
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 computing has transitioned from a largely theoretical construct into a rapidly evolving technological paradigm with significant implications for computational finance and financial data science. This article presents a comprehensive and integrative research synthesis examining the convergence of quantum algorithms, quantum machine learning, and probabilistic modeling techniques—particularly determinantal point processes—in the context of financial optimization, risk management, asset pricing, and data-driven decision-making. Drawing exclusively from a curated body of foundational and contemporary literature, the study explores how quantum computational advantages may address long-standing computational bottlenecks in finance, including high-dimensional optimization, stochastic simulation, portfolio construction, and time-series forecasting under uncertainty. The article begins by situating quantum finance within the broader evolution of financial computation, emphasizing the limitations of classical Monte Carlo methods, convex optimization frameworks, and deep learning architectures when confronted with combinatorial complexity and structural constraints inherent in modern financial systems. It then provides an extensive theoretical elaboration of quantum algorithms relevant to finance, including amplitude estimation, quantum portfolio optimization, stochastic optimal stopping, and martingale-based asset pricing in incomplete markets. Particular attention is devoted to algorithmic primitives such as low-depth amplitude estimation and near-term implementations on noisy intermediate-scale quantum hardware, highlighting their feasibility and constraints. A substantial portion of the article is dedicated to quantum machine learning models and their classical counterparts, examining parameterized quantum circuits, quantum-enhanced feature spaces, Bayesian quantum neural networks, and subspace-based learning. These models are critically compared with established classical approaches such as random forests, orthogonal neural networks, and tree-based ensembles, especially in the context of tabular financial data. The discussion is further enriched by an in-depth theoretical treatment of determinantal point processes as a unifying probabilistic framework for diversity-aware sampling, feature selection, and data imputation, including their classical and emerging quantum formulations. By synthesizing insights across quantum algorithms, machine learning theory, and financial engineering, this article articulates both the transformative potential and the unresolved challenges of quantum finance. It concludes with a forward-looking discussion on scalability, hardware constraints, hybrid quantum-classical workflows, and the epistemic implications of adopting quantum models in financial decision-making. The work aims to serve as a foundational reference for researchers and practitioners seeking a deep theoretical understanding of quantum computational finance as an emerging interdisciplinary field.
To address the practical impasse of mainstream economics—rooted in the deterministic worldview of Newtonian mechanics—where its mechanistic and linear analytical framework struggles to handle complex scenarios in the digital economy era, such as interactive decision-making, globally non-local correlations, and systemic mutations after sudden shocks, leading to a pronounced tension between theory and practice, this paper constructs a quantum economics analytical framework. Drawing on the inherent uncertainty revealed by quantum mechanics and based on the core isomorphism between economic systems and quantum systems (both centering on uncertainty, featuring superposition of multiple states, observer-induced interaction, and holistic correlations), it translates quantum core logics like superposition, entanglement, and transition into economic analytical tools. Methods such as entanglement entropy and quantum state transition probabilities are employed to quantify the complex correlations and state transformations of economic variables. Empirical studies of China’s macroeconomy validate the framework’s effectiveness, precisely capturing structural features like the strong correlation between money supply and economic growth, transmission frictions between money and prices, and paradigm shifts under policy shocks and event impacts. It further reveals the synergistic effect of a multi-variable system (“1+1+1>3”), quantifies the transitional characteristics and transformation laws of inflation states. The findings demonstrate that quantum economics elevates “uncertainty” to the theoretical core, transcending the paradigm limitations of traditional economics. It provides a novel quantitative perspective for understanding the complex essence and structural contradictions of economic systems, offering a systematic solution to the challenges of modern economic complexity.
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.