QNN Learning Algorithm to Diversify the Framework in Deep Learning

The QNN (quantum neural network) algorithmic approach is to interpreted the forward networking in the dataset. Alike, some classical dataset structure take input from the layer of qubit and directed to another layer of qubit. i.e. back propagation technique. The nature of QNN is an algorithmic based on parameter quantum circuit to train the dataset in an sequence manner by using classical optimizers to design an unique algorithmic computing technique in quantum learning, social network modeling, memory devices, automated control system, fast computing technique in executing program. It can be implemented in various sectors to learn hidden agenda layers -financial, health, economic etc. It decreases the limit of qubits, time & space complexity and compute within a second. It mainly optimizes the wave function, qubits representation in the QNN computing. The QNN is entitled for NISQ (noisy intermediate scale quantum computer) to remove the noise from the dataset. In machine learning dataset gather and trained in system with the model of QNN it enhanced the computing technique and optimizing strategies to remove noise from the data while modulating the signal approach. The layer of QNN introduces in this paper to approach the bit neural network, cellular network & associative neural network approach to classify the quantum learning optimization. This algorithmic approach implemented in the breast cancer dataset & predicts the 88.44 % accuracy on predicting the disease in patient. The aim of this paper is to categories the QNN properties in machine learning algorithm & provides new evidence of optimization.