A new statistical distribution, the Q of Fisher-Escolà, integrates quantum and classical probabilities to enable empirical testing of quantum theories of consciousness. Analyzing 150 density matrices of entangled states in a 10-qubit system on IBM quantum supercomputers, maximum likelihood estimation confirmed that the distribution follows a beta form. A novel analytical solution to the Quantum Fisher Information integral improved decoherence stability. Monte Carlo simulations established critical thresholds for significance levels; Type I errors occurred in 2-5% of right-tailed tests at α=0.05 and approached zero at stricter levels, while Type II errors in left-tailed tests were 1-4% at α=0.05 and also diminished. The framework enables hypothesis testing of quantum-classical interactions in consciousness research.
A large-scale, randomized, double-blind, multicenter trial across 13 hospitals in the UK and Spain tested whether the human mind can access information during clinical death when exposed to auditory stimuli governed by quantum entanglement, synchronized with a 127-qubit IBM quantum computer. During cardiopulmonary arrest, neurophysiological biomarkers were measured. After reanimation, 142 survivors completed recall tests and near-death experience scales. Recall lucidity increased as cerebral oxygenation dropped, and near-death experiences correlated with neuroplasticity during arrest. Biomarker models explained up to 56.8% of variance in recall. The authors argue these findings suggest consciousness may persist during clinical death, quantum-bound and detectable.
A new metric called the Attribution Consciousness Index (ACI) estimates the likelihood that neural activity supports conscious processing by balancing measures of dynamic information and complexity. Using brain simulations and artificial neural networks, the ACI follows a log-normal distribution, enabling robust thresholding: values above 10 correspond to over 90% probability of conscious emergence. The framework also applies to artificial systems, explaining 38.4% of variance between biological and AI-derived patterns. While not measuring subjective experience, the ACI predicts when neural or artificial conditions are poised to sustain consciousness, with potential applications in disorders of consciousness, anesthesia monitoring, neurorehabilitation, and evaluating neuroprosthetics, generative AI, and robotics.