Quantum computers have the potential to provide an advantage for financial pricing problems by the use of quantum estimation. In a broader context, it is reasonable to ask about situations where the market and the assets traded on the market themselves have quantum properties. In this work, we consider a financial setting where instead of by classical probabilities the market is described by a pure quantum state or, more generally, a quantum density operator. This setting naturally leads to a new asset class, which we call quantum assets. Under the assumption that such assets have a price and can be traded, we develop an extended definition of arbitrage to quantify gains without the corresponding risk. Our main result is a quantum version of the first fundamental theorem of asset pricing. If and only if there is no arbitrage, there exists a risk-free density operator under which all assets are martingales. This density operator is used for the pricing of quantum derivatives. To prove the theorem, we study the density operator version of the Radon-Nikodym measure change. We provide examples to illustrate the theory.
Quantum computers have the potential to provide an advantage for financial pricing problems by the use of quantum estimation. In a broader context, it is reasonable to ask about situations where the market and the assets traded on the market themselves have quantum properties. In this work, we consider a financial setting where instead of by classical probabilities the market is described by a pure quantum state or, more generally, a quantum density operator. This setting naturally leads to a new asset class, which we call quantum assets. Under the assumption that such assets have a price and can be traded, we develop an extended definition of arbitrage to quantify gains without the corresponding risk. Our main result is a quantum version of the first fundamental theorem of asset pricing. If and only if there is no arbitrage, there exists a risk-free density operator under which all assets are martingales. This density operator is used for the pricing of quantum derivatives. To prove the theorem, we study the density operator version of the Radon-Nikodym measure change. We provide examples to illustrate the theory.
A derivative is a financial security whose value is a function of underlying traded assets and market outcomes. Pricing a financial derivative involves setting up a market model, finding a martingale (``fair game") probability measure for the model from the given asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. We show that a variety of quantum techniques can be applied to the pricing problem in finance, with a particular focus on incomplete markets. We discuss three different methods that are distinct from previous works: they do not use the quantum algorithms for Monte Carlo estimation and they extract the martingale measure from market variables akin to bootstrapping, a common practice among financial institutions. The first two methods are based on a formulation of the pricing problem into a linear program and are using respectively the quantum zero-sum game algorithm and the quantum simplex algorithm as subroutines. For the last algorithm, we formalize a new market assumption milder than market completeness for which quantum linear systems solvers can be applied with the associated potential for large speedups. As a prototype use case, we conduct numerical experiments in the framework of the Black-Scholes-Merton model.
A derivative is a financial asset whose future payoff is a function of underlying assets. Pricing a financial derivative involves setting up a market model, finding a martingale ("fair game") probability measure for the model from the existing asset prices, and using that probability measure to price the derivative. When the number of underlying assets and/or the number of market outcomes in the model is large, pricing can be computationally demanding. In this work, we first formulate the pricing problem in a linear algebra setting, including the realistic setting of incomplete markets where derivatives do not have a unique price. We show that the problem can be solved with a variety of quantum techniques such as quantum linear programming and the quantum linear systems algorithm. While in previous works the martingale measure is assumed to be given, here it is extracted from market variables akin to bootstrapping, a common practice among financial institutions. We discuss the quantum zero-sum game algorithm and the quantum simplex algorithm as viable subroutines. For quantum linear systems solvers, we formalize a new market assumption milder than market completeness, which allows the potential for large speedups. Towards prototype use cases, we conduct numerical experiments motivated by the Black-Scholes-Merton model.
A central topic in economics and finance is the valuation of strategic games and bets, of investments providing uncertain future cashflows, and of other asset-like structures and contracts. Often, such valuation involves a probabilistic model, random variables, and computing expectation values of complex payoffs. Extending beyond this classical framework, a scenario emerges where assets are non-commuting operators and the inherent uncertainty is described by quantum states. In this work, we combine such notions of quantum finance with a graph-theoretic framework and ideas from contextuality. We discuss exclusive quantum assets and introduce scenarios when holding such assets can provide advantages over holding classical assets. In particular, we consider a "quantum market" based on nonlocal games such as the CHSH game and demonstrate that this setting can function as a market with well-defined financial structures. We provide upper bounds on the fair prices of the quantum assets based on graph-theoretic quantities like the independence number and the Lovász theta number. Our insights into markets of non-commuting assets help to elucidate what a financial system in a world filled with quantum technology could look like.
We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations between assets are naturally encoded in entangled quantum states, enabling exponentially compact representations of high-dimensional covariance structures. We derive theoretical bounds on the accuracy of quantum risk estimates and prove convergence theorems for quantum algorithms that compute value-at-risk, conditional value-atrisk, and higher-moment risk measures. Our framework incorporates counterdiabatic evolution techniques to navigate complex risk landscapes efficiently, establishing conditions under which quantum annealing approaches converge to optimal portfolio configurations with provable performance guarantees. We analyze the computational complexity of quantum versus classical risk calculation, identifying scenarios where quantum advantage emerges based on portfolio size, correlation structure complexity, and required solution precision. The theory extends to dynamic risk assessment under changing market conditions, proving that quantum algorithms can adapt to evolving risk profiles with polynomial overhead. We establish fundamental limits on quantum speedups for risk computation and identify problem structures that maximize quantum advantage. This theoretical foundation bridges quantum computing and quantitative finance, offering rigorous principles for designing quantum algorithms that address computational bottlenecks in modern risk management.
The relationship between expectation and price is commonly established with two principles: no-arbitrage, which asserts that both maps are positive; and equivalence, which asserts that the maps share the same null events. Constructed from the Arrow-Debreu securities, classical and quantum models of economics are then distinguished by their respective use of classical and quantum logic, following the program of von Neumann. In this essay, the operations and axioms of quantum groups are discovered in the minimal preconditions of stochastic and functional calculus, making this the natural domain for the axiomatic development of mathematical finance. Quantum economics emerges from the twin pillars of the Gelfand-Naimark-Segal construction, implementing the principle of no-arbitrage, and the Radon-Nikodym theorem, implementing the principle of equivalence. Exploiting quantum group duality, a holographic principle that exchanges the roles of state and observable creates two distinct economic models from the same set of elementary valuations. Advocating on the grounds that this contains and extends classical economics, noncommutativity is presented as a modelling resource, with novel applications in the pricing of options and other derivative securities.
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.