From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance

Battle Count: 53

The paper provides a conceptual framework for quantum AI in finance that is relevant to quantitative trading, particularly in modeling investor expectations, bounded rationality, and non-Bayesian information processing. However, it is primarily theoretical without empirical validation or implementable trading strategies. The quantum probability framework for expectation formation and the discussion of QNNs, QRL, and QAE for financial applications are conceptually relevant but not yet practically deployable. The paper's value lies in motivating future research directions rather than providing immediately actionable trading tools.

Extremely high complexity. The paper discusses quantum computing concepts (Hilbert spaces, superposition, entanglement, Born rule, interference terms) that require specialized quantum hardware or simulators. Current quantum computing infrastructure is nascent. The mathematical formalism (quantum probability calculus, non-commutative operators, state transitions) is advanced. Practical implementation would require expertise in quantum information theory, behavioral economics, financial modeling, and machine learning simultaneously. No code or implementation details are provided.

This is a theoretical/conceptual review paper with illustrative mathematical examples (Eqs. 1-11) but no empirical experiments, datasets, or code. The quantum probability framework is well-defined mathematically, but no specific implementation or benchmarking is provided. Reproducibility is limited to the mathematical formalism presented.