I. Introduction
The race to conquer the quantum information domain has accelerated the pace of research and innovation toward developing radical innovative approaches for quantum-based technologies. Quantum computing, communication, sensing combinatorial optimization, and quantum machine learning are among the most competitive and dynamic research and development areas currently on the verge of unlocking practical new quantum capabilities that will have a profound effect on almost every aspect of our lives. As classical data continues to grow faster than our ability to sense and make sense of it using classical computing resources. Quantum computing advantage accelerates the evolution of data from classical systems into quantum mechanical data produced through a natural quantum mechanical process that exhibits superposition and entanglement and/or when data is generated through a classical machine learning feature extraction, then loaded into a quantized process, transforming classical information into a quantum superposition state of n qubit to train and optimize a hybrid quantum-classical model as shown in the figure below.
Hybrid Quantum-Classical Machine Learning Model