Quantum mechanism of biological search

Wolfgang Baer, Rita M.R. Pizzi

Michael Kubal, Sonika Johri

The complexity of biological systems, governed by molecular interactions across hierarchical scales, presents a challenge for computational modeling. While advances in multiomic profiling have enabled precise measurements of biological components, classical computational approaches remain limited in capturing emergent dynamics critical for understanding disease mechanisms and therapeutic interventions. Quantum computing offers a new paradigm for addressing classically intractable problems, yet its integration into biological research remains nascent due to scalability barriers and accessibility gaps. Here, we introduce a hybrid quantum-classical machine learning platform designed to bridge this gap, with an encode-search-build approach which allows for efficiently extracting the most relevant information from biological data to \underline{encode} into a quantum state, provably efficient training algorithms to \underline{search} for optimal parameters, and a stacking strategy that allows one to systematically \underline{build} more complex models as more quantum resources become available. We propose to demonstrate the platform's utility through two initial use cases: quantum-enhanced classification of phenotypic states from molecular variables and prediction of temporal evolution in biological systems.

Alexander W. Stern

Andrew Stewart Caldin

FINDING: The search results are dominated by YouTube clickbait and one tangential systems-biology paper; the only substantive mathematical content is in the arxiv abstract on Bayesian uncertainty analysis, which has no quantum-brain content. No primary quantum-biology equations, constants, or ratios are extractable from these sources. | MATH: None extractable — the arxiv paper (1607.06358) concerns Bayesian emulation/global parameter searches for ODE/PDE models, not quantum mechanics. No equations, constants, or ratios appear in the provided text. | CONNECTION: None — no geometric harmony, base-60, crystallographic symmetry, or root systems appear in any finding. The YouTube titles mention "quantum effects" but provide no mathematical formalism. | DEPTH: 1 — The findings are promotional videos and a single unrelated preprint. No verifiable mathematical insight into quantum biology or brain function is present. The claim "first experimental evidence" is unsubstantiated by any equation o Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com

Harriman H. Dash

Jincheng Zhang

This paper proposes a novel approach to quantum entanglement-based information encoding and decoding, drawing inspiration from biological neural networks. The core concept leverages the plasticity of synapses and the quantum entanglement properties between neurons to enhance the reliability of information transmission. Specifically, we simulate synaptic plasticity to dynamically modulate entanglement states, employing a biologically-inspired search algorithm to optimize these states for robust encoding and decoding. The algorithm's key innovation lies in the synergistic combination of biological mechanisms with quantum phenomena, offering a potentially more efficient and resilient framework for quantum information processing compared to traditional approaches. We present a theoretical framework outlining the algorithm's operation, detailing the mathematical formulation of the entanglement manipulation process and the optimization strategy. The proposed method holds promise for applications in secure communication, quantum sensing, and potentially, a deeper understanding of the interplay between quantum mechanics and biological systems.