Fast and Illustrative Machine Learning Using the Quantum Fourier Transform

This protocol describes a Quantum Fourier Transform (QFT)-based computational method for analyzing multidimensional datasets and predicting dependent variables without using neural networks or iterative parameter optimization. Classical numerical data are normalized between zero and one, encoded as rotation angles using quantum RY gates, and processed using QFT on a quantum simulator or quantum hardware. The method generates paired X-Y relationships by applying QFT to feature vectors, both with and without the dependent variable. Housing price data were used to demonstrate the procedure, with property area, number of bedrooms, and number of bathrooms as input variables and house price as the dependent variable. QFT outputs obtained from the input features generated X coordinates, whereas outputs obtained after inclusion of the dependent variable generated Y coordinates. The resulting XY relationships were visualized using scatter plots and trendlines for prediction. The method was additionally applied to heart disease, card-transaction fraud, and loan-approval classification datasets. Quantitative validation using Python on two additional datasets produced reported prediction accuracies of 97.44% and 98.36%, with training times of 324 ms and 3.7 s, respectively. Simulator outputs were also compared with 1,024-shot measurements obtained using superconducting quantum hardware, demonstrating hardware-dependent differences under the tested conditions. These results demonstrate an empirical QFT-based approach for multidimensional data analysis and predictive modeling while identifying theoretical validation, hardware noise, and computational scalability as areas requiring further investigation.