Price, cost, and income (PCI) methods are traditionally used to approximate the value state of an economic commodity such as a property. Based on the estimates of these methods, we explore how quantum theory represents the fundamental process of value valuation in practice. We propose that the mathematical formalism of quantum theory is a promising view and measure of economic value. To ground our exploration, we first map traditional PCI estimates onto three-dimensional spherical coordinates, which were then transformed into two-dimensional quantum states using the Bloch sphere. This step enabled the computation of eigenvalues and eigenvectors of the Hamiltonian matrix, from which the value state measures were derived. The results exhibit practical applications as well as fundamental insights into potential connections between economic and quantum value states.
Price, cost, and income (PCI) methods are traditionally used to approximate the value state of an economic commodity such as a property. Based on the estimates of these methods, we explore how quantum theory represents the fundamental process of value valuation in practice. We propose that the mathematical formalism of quantum theory is a promising view and measure of economic value. To ground our exploration, we first map traditional PCI estimates onto three-dimensional spherical coordinates, which were then transformed into two-dimensional quantum states using the Bloch sphere. This step enabled the computation of eigenvalues and eigenvectors of the Hamiltonian matrix, from which the value state measures were derived. The results exhibit practical applications as well as fundamental insights into potential connections between economic and quantum value states.
We develop a quantum-style formalism for economic valuation in which the value of a good is represented by a wavefunction over price space. The observed price is associated with measurement of a price operator, while uncertainty in valuation is represented by the width of the valuation distribution. The state evolves according to a Schr\"odinger-type equation containing a valuation potential, a price-inertia term, and, for an open market, a non-Hermitian source/sink term. The purpose of the formalism is not to claim that financial markets are microscopic quantum systems. Rather, quantum mechanics provides a compact mathematical language for situations in which valuation is uncertain, sequential operations need not be interchangeable, and cyclic trading can reveal discrepancies that are invisible when each transaction is considered in isolation. We derive the canonical price--momentum commutator, the moment hierarchy, Ehrenfest equations, and a price--velocity uncertainty relation. We then introduce a lattice of mutually interacting assets through a neighbor-tension potential. The resulting model supports propagating valuation disturbances and provides a simple mathematical description of herding and synchronization. The principal new construction concerns arbitrage. Rather than identifying arbitrage directly with a geometric phase, we represent individual trading operations by operators. When two trading operations commute, their order is immaterial. When they do not commute, a round trip constructed from their group commutator fails to close in valuation space. This nonclosure is a natural mathematical signature of path dependence. An economically observable arbitrage opportunity arises when the resulting cycle transformation produces a positive net payoff after transaction costs. The formalism therefore connects four ideas: valuation uncertainty, neighbor interaction, noncommuting trading operations, and cyclic arbitrage. A harmonic valuation well provides a solvable benchmark, while a three-asset trading loop provides the simplest setting in which the distinction between ordinary price differences and genuinely noncommutative cycle effects becomes visible.
Abstract Since Adam Smith, economics has erected a grand edifice upon the foundations of rational agents, equilibrium analysis, and linear causality; however, the 2008 global financial crisis, the European sovereign debt crisis, and the COVID-19 pandemic have successively revealed its fundamental limitations when confronted with complex economic phenomena: bubbles that ought not to have existed, crises unforeseen by equilibrium models, non-linear ruptures brought about by technological revolutions, and self-fulfilling expectations—all of which call for a richer theoretical language. This book serves as the foundational introduction to the "Quantum Economics Textbook Series," as well as the first gateway into this entire series. It introduces the methodological framework, mathematical apparatus, and philosophical insights of quantum mechanics to economic analysis, grounding its approach in the core proposition of a practical ontology—that human economic practice itself possesses quantum attributes such as superposition, entanglement, constructibility, uncertainty, and emergence—thereby reconstructing the economic world as a quantum process of superposition and evolution. Six core concepts—superposition, entanglement, measurement collapse, the uncertainty principle, quantum transitions, and emergence—run throughout the work. Quantum economics does not represent a simple negation of classical theory; rather, it constitutes a dialectical sublation: under the classical limit—where decoherence is complete, the measurement basis is fixed, and entanglement vanishes—quantum theory precisely reduces to classical theory. This book comprises five parts and thirty chapters: Part I establishes the paradigm shift and theoretical framework, covering critiques of Newton's worldview, the quantum worldview, a foundational conceptual framework, mathematical foundations, and research methodologies; Part II reconstructs quantum microeconomic theory, encompassing consumers, producers, pricing, markets, the wave-particle duality of markets, quantum games, and welfare economics; Part III builds quantum macroeconomic theory, addressing national income accounting, money, inflation, economic cycles, policy, and open economies; Part IV explores quantum finance and market dynamics, including asset pricing, portfolio management, market sentiment, risk management, and trading theory; Part V extends the scope to organizations, innovation, institutions, development, economic ethics, and the digital economy, while offering insights into the future of this discipline. This volume is intended for undergraduate and graduate students, as well as researchers in economics and related fields, and serves as the essential first volume in the quantum economics series.
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 leverages fundamental principles of quantum mechanics to perform computations far exceeding classical architectures. At the core of single-qubit quantum state manipulation is the Bloch sphere, a geometric representation mapping the state space of two-level quantum systems onto a three-dimensional unit sphere. This paper presents a comprehensive theoretical framework of qubits, starting from single-qubit geometric state vectors and expanding to rigorous multi-qubit mathematical formulations. Furthermore, it analyzes single-qubit quantum gates as unitary rotations on the Bloch sphere, evaluates multi-qubit system scaling, and explicitly proves state normalization conditions using Born's rule across multi-dimensional Hilbert spaces.
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.
Mathematical finance explores the consistency relationships between the prices of securities imposed by elementary economic principles. Commonplace among these are replicability and the absence of arbitrage, both essentially algebraic constraints on the valuation map from a security to its price.
The discussion is framed in terms of observables, the securities, and states, the linear and positive maps from security to price. Founded on the principles of replicability and the absence of arbitrage, mathematical finance then equates to the theory of positive linear maps and their numeraire invariances. This acknowledges the algebraic nature of the defining principles which, crucially, may be applied in the context of quantum probability as well as the more familiar classical setting.
Quantum groups are here defined to be dual pairs of *-Hopf algebras, and the central claim of this thesis is that the model for the dynamics of information relies solely on the quantum group properties of observables and states, as demonstrated by the application to finance. This naturally leads to the study of models based on restrictions of the *-Hopf algebras, such as the Quadratic Gauss model, that retain much of the phenomenology of their parent within a more tractable domain, and extensions of the *-Hopf algebras, such as the Linear Dirac model, with novel features unattainable in the classical case.