Abstract Quantum Computing has emerged as one of the important dimensions of global research lately, on both the prospects, hardware as well as algorithms. With enhanced processing powers, several architectures based on adiabatic concepts resulting in reversibility have been proposed to date. Architectures based on Quantum Dot Cellular Automata have also shown considerable promise for realising the concept of reversibility. Recently, research has been focussed on the application of quantum computing for faster and secure communication. Dedicated machine learning algorithms and neural networks for quantum computation have also attracted considerable research. With a plethora of research and advances in this domain, this Special Issue publishes outstanding contributions for dissemination of the knowledge of Reversible Quantum Communication & Systems. This Special Issue publishes latest approaches and findings in Quantum Algorithms and Reversible Computing with focus on emerging Machine Learning approaches in Quantum Communications. Reversible Logic forms a pivotal part of Quantum Computing and has been a topic of high interest among Quantum Computing Scientists and researchers throughout the last decade. It also exhibits considerable prospects in recent research due to its adiabatic characteristics. Logic synthesis and optimisation algorithms within the purview of Reversibility have witnessed credible approaches and pose future prospects, such as the rise of Machine Learning approaches which have also penetrated the Quantum Domain.
Abstract Quantum Computing has emerged as one of the important dimensions of global research lately, on both the prospects, hardware as well as algorithms. With enhanced processing powers, several architectures based on adiabatic concepts resulting in reversibility have been proposed to date. Architectures based on Quantum Dot Cellular Automata have also shown considerable promise for realising the concept of reversibility. Recently, research has been focussed on the application of quantum computing for faster and secure communication. Dedicated machine learning algorithms and neural networks for quantum computation have also attracted considerable research. With a plethora of research and advances in this domain, this Special Issue publishes outstanding contributions for dissemination of the knowledge of Reversible Quantum Communication & Systems. This Special Issue publishes latest approaches and findings in Quantum Algorithms and Reversible Computing with focus on emerging Machine Learning approaches in Quantum Communications. Reversible Logic forms a pivotal part of Quantum Computing and has been a topic of high interest among Quantum Computing Scientists and researchers throughout the last decade. It also exhibits considerable prospects in recent research due to its adiabatic characteristics. Logic synthesis and optimisation algorithms within the purview of Reversibility have witnessed credible approaches and pose future prospects, such as the rise of Machine Learning approaches which have also penetrated the Quantum Domain.
Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications. This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks. Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network. This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas. We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography. The challenges and research directions in these domains are explored as well. Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.
In order to solve challenging optimization issues, analyze data effectively, and improve the capabilities of current machine learning algorithms, quantum computing has the potential to revolutionize the area of machine learning. The proposed work examines the fundamental ideas and methods of quantum computing—including quantum gates, quantum circuits, and quantum algorithms—as they relate to machine learning in this abstract. Various quantum computing applications in machine learning, including quantum neural networks, quantum support vector machines, and conventional methods influenced by quantum mechanics are also discussed. A review of state-of-the-art in quantum computing for machine learning, including recent advancements in quantum hardware and software has been done and the future prospects of this fascinating area has been examined.
Abstract — Reversible logic is one of the most vital issue at present time and it has different areas for its application, those are low power CMOS, quantum computing, nanotechnology, cryptography, optical computing, DNA computing, digital signal processing (DSP), quantum dot cellular automata, communication, computer graphics. It is not possible to realize quantum computing without implementation of reversible logic. The main purposes of designing reversible logic are to decrease quantum cost, depth of the circuits and the number of garbage outputs. This paper provides the basic reversible logic gates, which in designing of more complex system having reversible circuits as a primitive component and which can execute more complicated operations using quantum computers. The reversible circuits form the basic building block of quantum computers as all quantum operations are reversible. This paper presents the data relating to the primitive reversible gates which are available in literature and helps researches in designing higher complex computing circuits using reversible gates.
Recent times have witnessed tremendous competition towards quantum optics in business as well as academia, motivated by rapid growth in quantum computing. Overall scalability of quant present invention relates generally has exceeded several orders of magnitude, whereas ubiquitous quantum computers has accommodated as many as hundreds of quantum bits (i.e., thousands of qubits). Robust machines are still being created. Therefore, ethnicity has motivated an immense amount of research papers and reports. For all those people who read would really like to learn further the concepts of quant communication and computing from the machine learning perspective, this article provides an introduction. It begins with such an informative approach until continuing well over key turning points and most recent developments in quantum computing. These core features of such a virtual network are broken down into four significant issues in this paper, each of which has been closely investigated. A, B, C, and D are quantum mechanics, networking, security, and algorithms, accordingly. The article wraps up with the major challenges, the significant research areas, as well as the latest developments.
Quantum technology has recently emerged as a transformative computing paradigm. It is based on fundamental principles of quantum mechanics such as superposition, interference, and entanglement. This technology executes high computations that are impracticable by traditional computers. In recent years, developments in quantum technologies i.e algorithms, machine learning, cryptography, hardware design, and hybrid quantum classical frameworks have accelerated research and industrial interest. This paper provides a comprehensive review of quantum computing. It covers fundamental concepts, mathematical foundations, quantum algorithms, hardware platforms. It also focusses important applications, challenges, and emerging trends. The paper concludes with future research directions & the transformative potential of quantum technologies in computing, cryptography, optimization, and artificial intelligence.
Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows