Learning Quantum Computing

Saeed Mohsen, Gamal Elnashar, Mohamed H. Abdel-Aziz

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.

Vicente Moret‐Bonillo

J. Chazelas, Didier J. Decoster, Daniel Dolfi +2 more

Jae Pyoung Kim, Won Joon Yun, Hankyul Baek +1 more

As the technology of quantum computers experienced major advancements in recent years, Quantum computing (QC) has also naturally received an unprecedented amount of attention and the number of research on the topic has greatly increased as well. This is because compared to classical computing, QC is able to solve complex problems quickly and possess impressive computational capabilities. One of the fields which can exploit the strengths of QC is quantum machine learning (QML). QML integrates the field of QC and machine learning by applying QC to various areas of machine learning e.g., deep learning, reinforcement learning, and distributed learning. Despite the noise intermediate scale quantum (NISQ) limitation of QC, QML models still show superior performance compared to their classical counterparts by leveraging the strengths of QC. This has been thoroughly corroborated via extensive simulation results in many works which demonstrate the full potential and importance of QML. Therefore, in this paper, we aim to explore various QML methodologies e.g., quantum deep learning, quantum deep reinforcement learning, and quantum distributed learning. Furthermore, the model structures of each methodology will be discussed, highlighting the possibilities and limitations of each model. Finally, the direction of future works of QML research will be discussed as well.

Leonardo Tomazeli Duarte, Yannick Deville

Motivated by the potential performance gains offered by quantum computing (QC), a recent research focus — known as quantum-assisted machine learning (QAML) — aims to develop machine learning algorithms tailored for execution on quantum computers while processing data that originate from a classical (i.e. non-quantum) phenomenon. QAML has been studied in various quantum computing (QC) paradigms. This paper, in particular, offers a brief overview of QAML within the context of adiabatic quantum computing (AQC). The major challenge in this case is to reformulate the machine learning task as a quadratic unconstrained binary optimization (QUBO) problem, which lies at the core of existing AQC physical implementations. We provide examples of machine learning techniques tailored to AQC, along with applications in remote sensing.

H. David Sheets, Michael Dupin