Abstract

The tantalizing hypothesis that quantum phenomena underpin the brai's remarkable computational abil ities has sparked intense interdisciplinary interest. This study delves into quantum neuroscience by com putationally exploring quantum coherence in neural systems, aiming to uncover whether quantum effects enhance information processing. We developed a sophisticated model simulating a 1,000-qubit neural net work, with each qubit representing a neuron entangled under biologically relevant conditions (310 K, 0.15µs coherence time). Using IBM's Qiskit framework, we tested signal propagation efficiency, latency, and coherence duration across four conditions: quantum models with full, moderate, and high decoherence, and a classical benchmark. Our results reveal a striking 19.4% improvement in signal propagation efficien cy in the full-coherence quantum model (95.8% ± 2.9%) compared to the classical model (76.4% ± 5.1%; p < 0.001). Latency was reduced by 31%, with the quantum model achieving 0.68 µs versus 0.98 µs for the classical model. Coherence persisted for up to 1.5 µs, sufficient for short-range neural signaling. Ex tensive sensitivity analyses, varying temperature (300 - 325 K), noise (0.01 - 0.12 µs^-1), and network size (500 - 1,500 qubits), confirmed robustness, with efficiency remaining above 90% under moderate perturba tions. These findings suggest quantum coherence could complement classical neural mechanisms, potentially enhancing processes like sensory integration or consciousness. However, biological complexity, including biochemical interactions, warrants further exploration. We advocate for experimental validation using ad vanced quantum sensors, such as nitrogen-vacancy centers, to detect coherence in neural tissue. This study bridges quantum physics and neuroscience, offering a robust computational framework to probe the brain's quantum potential and inspiring future interdisciplinary research into cognition's mechanistic underpinnings.

DOI: doi.org/10.63721/25JPQN0105

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