Biologically Plausible Quantum Error Correction\\in a Three-Layer Neural Spin Model

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

Whether quantum coherence plays a functional role in neural information processing remains contested, largely because biological environments are deemed too noisy for quantum states to sur- vive at relevant timescales. We construct a three-layer model—nuclear spin memory, radical pair interface, classical electrochemistry—and identify five quantum error correction (QEC) paradigms that map onto known biophysical mechanisms: decoherence-free subspaces (DFS) via 31P singlet states, dynamical decoupling (DD) via protein motional narrowing, purification QEC (PQEC) ex- ploiting enzymatic redundancy (∼104 copies/cell), gauging symmetry protection via radical-pair spin conservation, and catalytic coherence recovery (ICEC) at enzyme active sites. We parametrize the model for two molecular systems: monoamine oxidase A (MAO-A; γeff = 4.55, radical pair un- confirmed) and Drosophila cryptochrome (CRY; γeff = 3.25, radical pair experimentally confirmed, |J| < 1 MHz). We quantify each mechanism individually: DFS provides complete protection against collective dephasing (F = 1.000), DD yields a B−2 0 -dependent relaxation suppression (4.6×107-fold at Earth field vs X-band), and PQEC achieves near-unity fidelity using 64 of ∼104 available copies. We integrate gauging, PQEC, and covariant recovery into a unified three-layer stabilizer and benchmark it on pattern classification, time-series prediction, and quantum-coherent decision dynamics. For MAO-A, the stabilizer significantly outperforms standard covariant QEC in classification (accuracy 85.2% vs 80.0%, p = 0.017, Cohen’s d = 1.19) and coherence preservation (+40% at moderate noise), while for time-series prediction the improvement is not statistically significant (p = 0.79). CRY yields statistically equivalent stabilizer performance (MNIST 86.2% vs 85.2%, p = 0.67; spike median MSE 7.31 vs 7.65, Mann–Whitney p = 0.99), demonstrating that the QEC framework is robust across molecular substrates. CRY’s decisive advantages are experimental: a confirmed radical pair and immunity to homeostatic neurotransmitter buffering. Four falsifiable experiments are proposed.

Article activity feed