Quantum Abduction: A New Paradigm for Reasoning under Uncertainty

2025-09-21

AI Summary

Traditional abductive reasoning treats hypotheses as mutually exclusive, relying on consistency checking or probabilistic updating to select a single optimal explanation—thus failing to model human capacities for concurrently maintaining contradictory hypotheses, reconciling inconsistencies, and synthesizing integrative explanations. To address this, we propose a quantum abductive framework grounded in quantum cognition theory: hypotheses are represented as superposed quantum states, enabling parallel coexistence; interference and measurement-induced collapse support evidence-driven dynamic fusion and non-eliminative reasoning. Methodologically, we integrate quantum state embedding, NLP-based semantic space modeling, and generative AI to realize computationally tractable hypothesis generation and synthesis. Empirical evaluation across historical mysteries, literary analysis, medical diagnosis, and scientific theory evolution demonstrates significant improvements in explanatory robustness under ambiguity and contradiction, as well as enhanced transparency of the inference process—establishing a novel paradigm for human-like creative abduction.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Abductive reasoning - the search for plausible explanations - has long been central to human inquiry, from forensics to medicine and scientific discovery. Yet formal approaches in AI have largely reduced abduction to eliminative search: hypotheses are treated as mutually exclusive, evaluated against consistency constraints or probability updates, and pruned until a single "best" explanation remains. This reductionist framing overlooks the way human reasoners sustain multiple explanatory lines in suspension, navigate contradictions, and generate novel syntheses. This paper introduces quantum abduction, a non-classical paradigm that models hypotheses in superposition, allows them to interfere constructively or destructively, and collapses only when coherence with evidence is reached. Grounded in quantum cognition and implemented with modern NLP embeddings and generative AI, the framework supports dynamic synthesis rather than premature elimination. Case studies span historical mysteries (Ludwig II of Bavaria, the "Monster of Florence"), literary demonstrations ("Murder on the Orient Express"), medical diagnosis, and scientific theory change. Across these domains, quantum abduction proves more faithful to the constructive and multifaceted nature of human reasoning, while offering a pathway toward expressive and transparent AI reasoning systems.

Problem

• Modeling abduction with hypothesis superposition instead of elimination
• Sustaining multiple explanatory lines while navigating contradictions
• Generating novel syntheses through constructive interference of hypotheses

Innovation

• Models hypotheses in quantum superposition
• Uses NLP embeddings and generative AI
• Supports dynamic synthesis over elimination