Reduction of latency in geospatial analysis of air quality: a classical modelling approach inspired by quantum...

Inspired by conceptual principles from quantum information theory, a novel classical approach to address temporal delays in spatial data analysis is presented. Current geospatial services face latency challenges due to complex processing chains, which motivate an investigation of whether the quantum-inspired paradigm could offer efficiency gains when implemented using classical hardware. A framework is proposed that incorporates three metaphors derived from quantum concepts: i) application of bit-like representation that mimics Qubit superposition to handle data uncertainty probabilistically; ii) use of probabilistic distributions for handling data uncertainty; and iii) creation of efficient data linkages by establishing pre-computed spatial correlations as an analogue to quantum entanglement. This model suggests potential temporal improvements while acknowledging current classical computing limitations. The proof-of-concept was tested on urban air quality monitoring, integrating data from fixed stations and mobile sensors. Simulation results indicated potential latency reduction while maintaining analytical accuracy (mean error <5.2% in controlled tests). Compared to the standard classical methods, the quantum-inspired metaphor showed efficiency improvements in theory when scaled to appropriate problem sizes, with simulated refresh rates of 250 milliseconds. Error analysis support the usefulness of the system for environmental health applications running on existing classical infrastructure. This research contributes: i) a framework for using quantum- inspired metaphors to address temporal challenges in geospatial analysis; ii) a simulation prototype for air quality monitoring; and iii) preliminary evidence of potential advantages from a bio-inspired approach in GIS processing. The technique may prove valuable for time-sensitive applications with today's technology and could inform future designs for potential quantum computing implementations.