Spatial Stochastic Resonance: Autonomous Blind Signal Recovery across High-Dimensional Potential Landscapes

Abstract

Executive Summary In traditional signal processing, capturing sub-threshold signals buried deep beneath the noise floor (SNR < 0 dB) represents an insurmountable wall for linear filters and digital Fourier estimators. While classical Stochastic Resonance (SR) has long promised a noise-enabled sensing path, its industrial adoption has been paralyzed by the "Tuning Bottleneck"—the requirement for precise manual noise calibration and strict prior knowledge of target frequencies. This technical white paper formalizes Spatial Stochastic Resonance (SSR), a transformative physical computing paradigm synthesized within a 100,000-node Resonance Processing Unit (RPU) continuum. By imprinting stratified potential energy landscapes (Vbias) into the physical substrate, SSR enables autonomous ambient noise harvesting, converting hostile interference into the kinetic energy required for deterministic signal revelation. Technical Core & Methodology Stratified Potential Landscapes (Vbias): A multi-scale resonance sieve featuring heterogeneous barrier depths that ensures autonomous matching for arbitrary noise intensities without manual recalibration. Temporal Integration Protocol: A deterministic 4,000-step physical integration cycle that facilitates macroscopic phase-locked accumulation, allowing weak coherent rhythms to emerge while disordered noise is spatially dissipated. Internal Physical Tension (T): A scalar physical invariant providing a zero-computation anomaly detection trigger, enabling carrier lock-on for completely unknown signals. Author: QuantNature GlobalRelated Reference: The Resonant Computer: A Post-Digital Computing Paradigm via Volumetric Physical Wave Interference (DOI: https://doi.org/10.5281/zenodo.21886803)