A Tutorial on Quantum Computing and Deep Learning with Quantum Deep Neural Networks

Quantum Computing (QC) technology and Deep Learning (DL) science have garnered significant attention for their potential to revolutionize computation. This paper introduces the basic concepts and terminologies of QC and DL to provide an understanding of these fields. In the context of QC, we explain key concepts such as qubits, quantum superposition (QS), quantum gates (QG), quantum measurement (QM), quantum circuits, quantum algorithms (QA), and different quantum computing applications. Regarding the DL, we present also the main concepts about various types of learning, DL models, hyperparameters of DL models, and DL applications. Also, this paper highlights the main differences between quantum deep learning (QDL) and classical deep learning (CDL). Additionally, we describe the required sequential steps to construct a quantum deep neural network (QDNN) model. This paper presents a valuable reference for beginners and researchers to a quick understanding of QC and DL and seeking to know about the latest developments in these rapidly advancing fields.