Quantum neurons with real weights for diabetes prediction

Abstract

Parametric models with real numbers valued parameters have greater performance

than its counterparts with binary valued weights, due to the gain in representing informa- tion with real values, and therefore having a larger space for memory association. In this

work, is proposed a quantum neuron capable of store real weights and preserve the gain of the superposition property, encoding the information in the probability amplitudes of the quantum system, the Real Weights Quantum Neuron. Its performance is compared with other quantum neurons to analyze the application of the quantum neurons on real-world problems, i.e diabetes classification. The results of the experiments shows that a single quantum neuron is capable of achieving an accuracy rate of 100% in the XOR problem and an accuracy rate of 100% in a non-linear dataset, demonstrating that the quantum neurons with real weights are capable of modeling non-linearly separable problems. In the problem of diagnosing diabetes, quantum neurons achieved an accuracy rate of 76% and AUC-ROC of 88%, while its classic version, the perceptron, reached only 63% accuracy and the artificial neural network reached 80% AUC-ROC. These results indicate that a single quantum neuron performs better than its classical version and even the artificial neural network for AUC-ROC, demonstrating potential for use in healthcare applications in the near future. This work is also a contribution to the field of quantum neural networks, which can be further advanced from the quantum neuron proposed.