This approach demonstrates exponential efficiency gains in asset pricing models using quantum properties, suggesting advancements in financial modeling.
We formulate quantum computing solutions to a large class of dynamic nonlinear asset pricing models using algorithms, in theory exponentially more efficient than classical ones, which leverage the quantum properties of superposition, entanglement, and interference. The equilibrium asset pricing solution is a quantum state. We use quantum decision‐theoretic foundations of ambiguity and model/parameter uncertainty to deal with model selection.
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Ghysels et al. (2025) studied this question.
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