Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2601.18435

The Quantum Cliff: A Critical Proton Tunneling Threshold Determines Clinical Severity in RPE65-Mediated Retinal Disease

Biraja Ghoshal·William Woof·Bhargab Ghoshal·Nikolas Pontikos
View original source ↗Read source document ↗Export metadata ↓

Abstract

Predicting clinical severity from genotype remains a fundamental challenge in molecular medicine, particularly for enzymes whose function depends on sub-atomic-scale geometry. Mutations in the \textit{RPE65} isomerohydrolase cause Leber Congenital Amaurosis (LCA) and related retinal diseases; however, the kinetic mechanisms connecting sub-atomic-scale perturbations to blindness remain unclear. In this study, we demonstrate that mutations in the human visual isomerase RPE65 are governed by a quantum-mechanical threshold effect arising from proton tunneling in the active site. We established a hybrid quantum-classical structure-to-phenotype pipeline combining AlphaFold structure prediction with \textit{ab initio} quantum simulation using the Variational Quantum Eigensolver (VQE) to analyze minimal proton-coupled electron transfer in the visual cycle. Our analysis reveals that many pathogenic mutations do not merely occlude the active site, but rather strongly reduce the quantum probability of proton tunneling. We observed a sharp non-linear effect, termed the "Quantum Cliff," where minute structural changes (below 0.1 Å) reduce the reaction rate by multiple orders of magnitude. Based on these findings, we introduce a dimensionless Relative Quantum Activity Score (RQAS) that isolates the geometry-controlled exponential sensitivity of the reaction rate and successfully distinguishes between mild and severe patient phenotypes. These results suggest that RPE65 operates near a quantum-critical point, where sub-Angstrom structural perturbations induce a catastrophic loss of function. Furthermore, our findings establish quantum tunneling as a predictive mechanistic link between atomic structure and clinical phenotype, proposing a general framework for quantum-structural disease modeling.

Explore related subjects

cs.ETq-bio.BM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Biraja Ghoshal, William Woof, Bhargab Ghoshal, Nikolas Pontikos. 2026-02-08. The Quantum Cliff: A Critical Proton Tunneling Threshold Determines Clinical Severity in RPE65-Mediated Retinal Disease. https://arxiv.org/abs/2601.18435

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Beyond HBM-on-GPU: Thermal Design Envelope for 3D Volumetric DRAM-on-GPU Integration

The scaling of GPUs for AI and HPC workloads is increasingly constrained by the capacity, bandwidth, and thermal limits of both 2.5D HBM-GPU and direct-stacked 3D HBM-on-GPU integration. This work establishes the thermal design envelope for 3D volumetric DRAM-on-GPU integration, in which vertically oriented DRAM dies and interleaved cooling cavities reshape heat flow and memory interfacing above the GPU. Using a package-level thermal model anchored to a consistent HBM-on-GPU baseline and driven by a realistic reticle-scale non-uniform GPU power map, we quantify the key parameters governing thermal feasibility. Stack height is the dominant limiter of peak temperature, while cooling-cavity conductivity shifts the feasible region, and mold insertion and stack orientation further modulate thermal behavior. A distributed memory-controller and network-on-chip tier introduces only a moderate thermal penalty. Although die-level parallelism increases bandwidth, the reduction in simulated training time saturates once execution becomes compute-bound. These results define a bounded co-design space across bandwidth, capacity, and thermal constraints for 3D volumetric DRAM-on-GPU integration.

cs.ET↗
arXiv2026

Droop-Aware Foundation Model Power Flow

This paper develops a droop-aware extension of the GridFM power systems foundation model, embedding droop gains and frequency/voltage deadband parameters as per-bus node features to enable control-aware AC power-flow analysis. Existing power-flow datasets encode only static electrical features, conflating operating points from qualitatively different control regimes; this work resolves that gap by exposing droop and deadband parameters as structured node features, with deadband discontinuities handled through a smooth tanh approximation that preserves solver differentiability. A transformer-based graph neural network is pre-trained on masked reconstruction and fine-tuned on the resulting control-aware datasets. The framework is validated against PSCAD electromagnetic-transient simulations on a two-bus system (0.11% maximum steady-state error) and cross-validated against an independent PyPower droop solver on the IEEE 24-bus RTS. On the 24-bus system the surrogate attains R2 = 0.9996 for active generation and 0.0015 p.u. voltage-magnitude RMSE; scalability is confirmed on the IEEE 300-bus system (0.0036 p.u. RMSE, R2 = 0.9841 for voltage magnitude across 299,700 predictions). A three-mode control study further shows that the deadband widens the control-error distribution while leaving total droop compensation unchanged, establishing deadband width as an actionable node-level design feature.

cs.ET↗
arXiv2026

GNN-based Path-aware multi-view Circuit Learning for Technology Mapping

Traditional technology mapping suffers from systemic inaccuracies in delay estimation due to its reliance on abstract, technology-agnostic delay models that fail to capture the nuanced timing behavior behavior of real post-mapping circuits. To address this fundamental limitation, we introduce GPA(graph neural network (GNN)-based Path-Aware multi-view circuit learning), a novel GNN framework that learns precise, data-driven delay predictions by synergistically fusing three complementary views of circuit structure: And-Inverter Graphs (AIGs)-based functional encoding, post-mapping technology emphasizes critical timing paths. Trained exclusively on real cell delays extracted from critical paths of industrial-grade post-mapping netlists, GPA learns to classify cut delays with unprecedented accuracy, directly informing smarter mapping decisions. Evaluated on the 19 EPFL combinational benchmarks, GPA achieves 19.9%, 2.1% and 4.1% average delay reduction over the conventional heuristics methods (techmap, MCH) and the prior state-of-the-art ML-based approach SLAP, respectively-without compromising area efficiency.

cs.ET↗