QISFP: a quantum-inspired semantic fidelity protocol for energy-efficient and noise-resilient visual IoT transmission

Abstract

Abstract Visual Internet of Things deployments operate under coupled bandwidth, energy and channel-reliability constraints, yet conventional video coding optimises pixel-level reconstruction rather than the preservation of task-relevant meaning. This paper presents QISFP, a quantum-inspired semantic fidelity protocol that treats semantic degradation as a stochastic state transformation rather than as additive signal distortion. It couples delta encoding of temporally associated object-level semantic states with a stochastic channel built from classical analogues of the depolarizing and amplitude-damping maps, and reconstructs meaning by energy- and confidence-weighted fusion over a temporal window. The framework is classical throughout: the quantum formalism supplies trace-preserving maps whose diagonal restriction is a stochastic matrix, which keeps per-object cost linear rather than quadratic in the semantic dimension. The induced class channel is shown to be $$M$$ M -ary symmetric, yielding two independent decay constants that characterise a semantic link and fix a multi-hop relaying budget, while a source-channel separation argument gives the attainability limit $$F \le 1 - p_d(1-1/M)$$ F ≤ 1 - p d ( 1 - 1 / M ) , met with equality by uncoded single-observation transmission; every attainable gain therefore lies in the temporal dimension. Simulation on CDNet 2014 and UCF-101 indicates 27.5% lower energy per frame and 30.3% lower transmitted volume than full-frame x264, with semantic fidelity of 0.922 against 0.855 for a frame-wise semantic baseline, at a cost of 1.28 dB in PSNR. An end-to-end measurement on detector output tests the theory directly: the frame-wise baseline tracks the single-use limit to within 0.022 IoU, the framework exceeds that limit wherever the channel is non-trivial, and at matched fidelity it requires between 42% and 64% fewer bits than the baseline. The protocol requires a few kilobytes of state and fewer than $$10^4$$ 10 4 operations per frame, so embedded feasibility is governed by the choice of semantic extractor rather than by QISFP itself.

The authors' abstract, as published at the source. Journal of Reliable Intelligent Environments, 2026 · DOI ↗