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

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.