This chapter explores a new paradigmatic thinking in financial market behavior, namely, quantum-like framework (QLF), which is a philosophical, mathematical and information-theoretic extension of quantum science formulation to financial market behaviors. Complexity science has already enriched financial market understanding based on network and chaos theories; however, QLF can further enrich such understanding based on a concrete framework of radical uncertainty and ambiguity, which is analogically inspired by quantum physics (rather than any physicalist or reductionist approach). The chapter also provides insights for open quantum system theory-based analysis of financial market interactions, quantum game theory applications and quantum computation applications in financial markets for future.
This chapter explores a new paradigmatic thinking in financial market behavior, namely, quantum-like framework (QLF), which is a philosophical, mathematical and information-theoretic extension of quantum science formulation to financial market behaviors. Complexity science has already enriched financial market understanding based on network and chaos theories; however, QLF can further enrich such understanding based on a concrete framework of radical uncertainty and ambiguity, which is analogically inspired by quantum physics (rather than any physicalist or reductionist approach). The chapter also provides insights for open quantum system theory-based analysis of financial market interactions, quantum game theory applications and quantum computation applications in financial markets for future.
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 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 chapter explores the quantum-like characteristics of human psychology in financial decision-making, revealing how individual cognitive biases create collective market behaviors that exhibit superposition, entanglement, and observer effects. The quantum psychology hypothesis suggests that investor decision-making mirrors quantum mechanics: individuals simultaneously hold multiple conflicting beliefs about market conditions until external events force a resolution into specific positions. The analysis demonstrates how observation changes reality in financial markets through three mechanisms: analyst reports that influence the very companies they analyze; investor sentiment that becomes self-fulfilling through collective action; and measurement effects, where market research alters the behaviors being studied. Three detailed case studies illuminate these principles: the 2020–2022 cryptocurrency boom showing how the fear of missing out (FOMO) created quantum-like feedback loops; the dot-com bubble demonstrating valuation superposition, where companies existed simultaneously as revolutionary and worthless until market forces collapsed these contradictions; and the 2008 housing crisis revealing how cognitive biases maintained conflicting beliefs about property values. The chapter develops frameworks for understanding market sentiment as quantum coherence and decoherence phases in which consensus breaks down, leading to volatility. These insights provide tools for navigating crowd psychology and market dynamics that extend far beyond traditional behavioral economics.
Is mind quantum-like? Is consciousness quantum physical? Does consciousness have a role to play in the emergence of the classical world from the underlying quantum universe? Or is this dichotomy between classical and quantum an illusion? How can world philosophies enrich our views on these matters and interact with the best scientific theories? These are some questions that motivated us to conceive the current book. We also felt an urgent need to discuss the central philosophical questions haunting the emerging frameworks of quantum cognition and quantum-like framework (QLF). We have observed that there are very good technical or mathematical works being done in the emerging fields of quantum cognition and QLF, but there is still a great need for exploring the underlying and open philosophical questions they demand. Quantum cognition developed as a frontier research programme in understanding various cognitive phenomena that are hard or impossible to describe based on classical cognitive science paradigm. Classical cognitive science draws philosophically and mathematically from classical physics and classical probability theory. For example, cognitive realism thinking is deeply motivated by the scientific realism of classical physics. However, as we explore, there are numerous empirical findings which demonstrate clearly that human mind and cognitive processes exhibit features that are more quantum-like. Central to these features are indeterminacy, context dependence, and complementarity of cognitive phenomena, for example, perception, information retrieval, and even qualia. Many good works in the previous decade have demonstrated the efficacy of quantum-like mathematical modelling in such cases. However, given that efficacy, we cannot simply conclude that mind is quantum. Rather there are features that go beyond what is allowed in standard quantum formalism.
The increasing complexity of modern decision-making environments has challenged the effectiveness of classical mathematical models. Traditional probabilistic frameworks often struggle to represent uncertainty, ambiguity, contextual dependence, and nonlinear interactions among decision variables. Quantum-inspired mathematical models have emerged as a promising interdisciplinary approach that leverages mathematical principles derived from quantum theory without requiring quantum computing hardware. These models utilize concepts such as superposition, interference, entanglement, and contextuality to represent complex decision processes more accurately. This paper explores the theoretical foundations, mathematical structures, and applications of quantum-inspired decision models in complex systems. The study examines how quantum probability theory provides a more flexible representation of human cognition, organizational decision-making, financial forecasting, and artificial intelligence systems. Furthermore, the paper discusses mathematical formulations, implementation strategies, advantages, challenges, and future research directions. The findings suggest that quantum-inspired models offer significant potential for addressing uncertainty and dynamic interactions in complex decision environments, thereby contributing to the advancement of intelligent decision support systems.
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.