An Extended Epistemic Framework Beyond Probability for Quantum Information Processing with Applications in Security, Artificial Intelligence, and Financial Computing

In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characteriz...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 27; no. 9; p. 977
Main Author: Iovane, Gerardo
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 18.09.2025
Subjects:
Algorithms
Artificial intelligence
Credibility
Cybersecurity
Data processing
Decision making
Entropy
epistemic uncertainty
Epistemology
extended probability models
Fuzzy logic
Hilbert space
Information processing
Investment analysis
Multivalued logic
plausibility and credibility
possibility theory
Probability
Quantum computing
quantum decision-making
Quantum entanglement
quantum information theory
Quantum phenomena
Quantum physics
Semantics
Statistical analysis
Uncertainty
quantum information theory
epistemic uncertainty
fuzzy logic in quantum systems
extended probability models
non-Kolmogorovian reasoning
quantum decision-making
quantum-enhanced applications
hybrid classical–quantum inference
possibility theory
plausibility and credibility
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e27090977