In this article we briefly introduce the theory and development of DNA-based computing, present the underlying problems associated with DNA-based computing technology, and discuss the trend of nanoelectronics as well as several computational paradigms for selected existing classical solutions with quantum-effect devices. The pros and cons of both the DNA-based computing and the quantum computing are discussed and compared from the error resistant viewpoint and at the system architecture level.
In this article we briefly introduce the theory and development of DNA-based computing, present the underlying problems associated with DNA-based computing technology, and discuss the trend of nanoelectronics as well as several computational paradigms for selected existing classical solutions with quantum-effect devices. The pros and cons of both the DNA-based computing and the quantum computing are discussed and compared from the error resistant viewpoint and at the system architecture level.
Building on the fundamental principles of quantum computing, this paper sequentially presents the development of quantum computing and its associated application domains. We also analyze and compare the differences between quantum and classical computing, highlighting the benefits of quantum computing for specific problems. To comprehend the advantages of quantum computing in particular fields, extensive research has been conducted in three areas: cryptography, chemistry, and artificial intelligence. We also discuss the wider applications of this emerging technology for future investigation, considering the current state of quantum computing development and anticipated trends.
Quantum computing represents a paradigm shift in computational technology, promising to revolutionize the way we approach complex problems across various domains. Unlike classical computing, which relies on bits as the smallest unit of information, quantum computing leverages quantum bits or qubits, which can exist in multiple states simultaneously due to the principles of superposition and entanglement. This article delves into the potential of quantum computing to address real-world complex problems, exploring its theoretical foundations, current advancements, and future prospects. We examine key areas where quantum computing could have a transformative impact, including cryptography, optimization, drug discovery, artificial intelligence, and climate modeling. Additionally, we present three detailed tables summarizing the comparative advantages of quantum computing over classical methods, the current state of quantum hardware, and the potential applications of quantum algorithms. The article concludes with a discussion on the challenges and ethical considerations associated with the widespread adoption of quantum computing.
Quantum computing represents a paradigm shift in computational power, leveraging quantum mechanics principles to solve complex problems beyond the reach of classical computers. As quantum technologies evolve, they hold the promise of revolutionizing industries ranging from cryptography and artificial intelligence to material science and drug discovery. This article explores the current developments in quantum computing, highlighting the potential future implications for various sectors. The article discusses the state-of-the-art quantum computing techniques, key challenges in scalability and error correction, and the potential for real-world applications. The implications of quantum computing on cryptography and data security, coupled with advancements in quantum algorithms, are also examined. The future of quantum computing is bright, but significant challenges remain that will require collaboration across interdisciplinary fields.
A new paradigm of computation has appeared recently that is known as quantum computing and which is able to solve difficult problems that cannot be solved using classical computing.It provides a comprehensive overview of the basic concepts, computational models, architectures, tools, applications and implications of quantum computing.The paper is based on the latest research, and it's used to evaluate the state of quantum computing, from theory to practical, application-oriented implementations.Fundamental concepts such as qubits, superposition, entanglement and hybrid quantum-classical computation are explained, along with major models and frameworks of quantum computing, including Cirq, TensorFlow Quantum and ProjectQ.Furthermore, various significant application areas, such as cryptography, optimization, AI, drug discovery, and industrial computing, are explored, highlighting the applications' potential and their technological development.Although great strides have been made, scalability, noise, error correction, interoperability and hardware challenges are major problems in the NISQ era.It also examines new trends, future research directions and the societal, security and ethical considerations of the advancement of quantum technology.But the bigger picture indicates that quantum computing is progressing from theory to reality, and there are many technical and institutional challenges to be resolved along the way.Interdisciplinary studies and responsible innovation will be key to unlocking the potential of quantum computing as the next generation of computational technology.
This paper presents a comprehensive review of quantum computing and the technological foundations of quantum processors. Quantum computing represents a transformative computational paradigm that leverages the fundamental principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to solve complex problems that are intractable for classical computers. At the core of this paradigm lies the quantum processor, a specialized hardware system designed to initialize, control, and measure quantum bits (qubits) through precise gate operations and coherent interactions. The survey examines the essential theoretical concepts underlying quantum computation and provides a structured comparison of leading quantum processor platforms. Prominent architectures, including superconducting circuits, trapped-ion systems, and photonic implementations, are analyzed in terms of their physical mechanisms, scalability, coherence properties, and engineering challenges. Each platform offers distinct advantages and faces specific technical limitations related to noise, error correction, and fabrication complexity. In addition to reviewing hardware developments, this study highlights key obstacles in advancing quantum processors toward large-scale, fault-tolerant systems. Issues such as qubit stability, error mitigation, thermal management, and system integration are discussed to present a realistic perspective on current progress. Performance indicators including gate fidelity, coherence time, connectivity, and scalability are used to compare architectural approaches. The objective of this paper is to provide a clear and organized synthesis of recent advancements in quantum computing hardware, thereby offering researchers and practitioners a foundational reference to support continued innovation and development in this rapidly evolving field.
This chapter deals with the transforming advances in intelligent quantum computing, considering the major emphasis on the synergistic relation-ship between the classical and quantum computational paradigms. It first considers the basics of quantum principles such as superposition, entanglement, and quantum gates that form the cornerstone for quantum computing. Further, the chapter portrays the development of quantum architectures such as variational quantum eigensolvers and the framework of distributed quantum computing to effectively handle the challenge of large-scale computation. The main issues that have been discussed pertain to quantum error correction techniques developed and put into place, which are designed to sustain system fidelity. Advanced quantum algorithms were presented, including QAOA, whose solutions for optimization problems exist. Machine learning integrated with quantum systems is discussed as well, showing the potential of hybrid approaches to solve challenges in data-intensive environments. It gives an overview of the insights from experimental investigations into quantum gates, scalable quantum systems, and quantum memory. These studies emphasize the optimization of performance, minimizing computational overheads, and overcoming current technological limitations. Applications of quantum computing in real life are discussed, underlining the transformative potential of quantum computing in cryptography, chemical modeling, and artificial intelligence. The chapter concludes with a look to the future in the field, presenting trends and challenges such as scalable implementation and quantum-classical integration. This chapter thus lays a critical foundation for both researchers and practitioners in making sense of not only the present status but also the future possibilities in intelligent quantum computing, thereby encouraging further innovation and exploration in this fast-advancing domain.