Quantum Fourier Transformations to Decompose and Restructure Low-Cost Chemoresistive Gas Sensor Data
By Amit Sai Erraguntla, Arnav Jain, Nikhil Pesaladinne
Current air quality sensors are too expensive and are not feasible to use on a regular basis. While cheaper sensors exist, they output resistances to various gases rather than a measure of air quality. Previous attempts to restructure the gas resistance data using machine learning models overfit and fail to extrapolate to outside data. Since the sensor data is slightly sinusoidal, we plan to use quantum Fourier transformations to estimate a function for air quality in a more robust manner.
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