Hybrid Classical-Quantum Neural Network for n-Dimensional Embedding

Embedding of n-dimensional data in quantum space is an essential component in quantum computing realm. This research area is quite challenging with limited computational resources and complex quantum architecture. To overcome these problems, quantum approaches have been integrated with classical neural network. This hybrid model encodes the features using both angle encoding and amplitude encoding over quantum hilbert space, which is then processed through a Variational Quantum Circuit (VQC). These embedded features are fed to the classical neural network that enhances the performance of the traditional methods. A synthetic barcode and stripes (BAS) dataset was created for the verification of the proposed model. The results show that classical optimization methods, specifically gradient descent with momentum, achieve an accuracy of 82.38% for 4×4 data which is a significant improvement over existing researches that are limited to lower-dimensional data embedding.