Quantum Consciousness Collapse Model (QCCM): A Unified Framework for Quantum Neural Processing and Cognition

Abstract: Quantum Consciousness Collapse Model (QCCM)

The Quantum Consciousness Collapse Model (QCCM) proposes that transient quantum fluctuations—occurring on femtosecond timescales—play a fundamental role in neural processing, cognition, and decision-making. Unlike prior models such as Orch-OR, which rely on sustained quantum coherence, QCCM integrates stochastic resonance, neural oscillatory synchronization, and metabolic-thermal modulation to explain how quantum effects persist in biological systems despite decoherence challenges.

The theoretical foundation of QCCM rests on the idea that quantum fluctuations within neural microtubules or related substructures are amplified via stochastic resonance, allowing them to influence synaptic activity and neural dynamics. These quantum fluctuations then become phase-locked with gamma oscillations (40–100 Hz), effectively synchronizing transient quantum events with classical neural computations. Additionally, metabolic energy levels and temperature fluctuations modulate neural entropy, influencing the persistence of quantum coherence in cognition.

This paper introduces a mathematical model for quantum stochastic resonance in neural circuits and derives a temperature-dependent decoherence equation that predicts a 23% extension of coherence times at biological temperatures (37°C vs. 25°C). Furthermore, we demonstrate the superiority of quantum probability models in human decision-making tasks, where quantum models achieve an AUC of 0.92 compared to 0.76 for classical Bayesian models.

To validate QCCM, we propose a rigorous experimental roadmap, including:

1. Ultrafast spectroscopy and SQUID/NV-center quantum sensors to detect femtosecond quantum coherence in neurons.
2. EEG-based Bell tests to measure potential entanglement across neural networks.
3. Neuroimaging and metabolic modulation experiments to assess the impact of energy states on neural entropy and quantum signatures.
4. Quantum machine learning simulations to refine and test cognitive probability models.

In addition to its neuroscientific implications, QCCM has broad applications in artificial intelligence (AI), clinical diagnostics, and theoretical physics. We introduce the Quantum Coherence Index (QCI) as a potential clinical biomarker for consciousness disorders, offering new diagnostic tools for conditions such as comas and locked-in syndrome. Furthermore, the model raises ethical questions regarding artificial consciousness, proposing threshold criteria for quantum-based AI systems.

With detailed experimental controls, interdisciplinary validation strategies, and an emphasis on reproducibility, QCCM provides a testable and scalable framework for investigating the quantum foundations of cognition. If confirmed, this model could revolutionize neuroscience, bridge quantum mechanics with biological computation, and redefine our understanding of consciousness.