Based on the rapid development of quantum computing and quantum communication all over the world, this paper studies the latest development of quantum computing on the basis of consulting a large number of literatures. By analyzing literature and summarizing, the basic knowledge and concept of quantum computing, the theory of time and data complexity, quantum computer, and the technical route of quantum computing are studied. Through the research, it is found that quantum computing is still in the primary stage, and the hardware of quantum computing, quantum error correction, anti-noise quantum computing and quantum simulation all need further research to improve quantum theory.
Based on the rapid development of quantum computing and quantum communication all over the world, this paper studies the latest development of quantum computing on the basis of consulting a large number of literatures. By analyzing literature and summarizing, the basic knowledge and concept of quantum computing, the theory of time and data complexity, quantum computer, and the technical route of quantum computing are studied. Through the research, it is found that quantum computing is still in the primary stage, and the hardware of quantum computing, quantum error correction, anti-noise quantum computing and quantum simulation all need further research to improve quantum theory.
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.
Quantum computing is an emerging paradigm that exploits principles of quantum mechanics—such as superposition, entanglement, and tunneling—to process information in ways that classical computers cannot. Rapid progress in quantum hardware, quantum algorithms, and error-correction research suggests transformative potential across fields such as cryptography, optimization, materials science, and machine learning. This review summarizes fundamental concepts, leading hardware platforms, algorithmic milestones, current challenges, and future trajectories in the development of large-scale, fault-tolerant quantum computers.
Quantum computing is an emerging computational paradigm that uses the principles of quantum mechanics, including superposition, entanglement, and quantum interference, to process information in fundamentally different ways from classical computers. During the last decade, quantum computing has progressed from theoretical research toward experimental and cloud-accessible systems. Recent developments have focused on increasing the number and quality of qubits, reducing gate errors, developing quantum error correction, improving quantum software, and integrating quantum processors with classical high-performance computing systems. Despite significant progress, practical large-scale quantum computing remains challenging because quantum states are extremely sensitive to environmental disturbances, resulting in decoherence and computational errors. Other challenges include limited qubit coherence time, gate fidelity, scalability, cryogenic requirements, quantum error correction overhead, hardware connectivity, programming complexity, and the difficulty of demonstrating useful quantum advantage for real-world applications. Recent developments from major research organizations indicate a shift from simply increasing physical qubit counts toward building reliable logical qubits and fault-tolerant quantum systems. Google has reported progress toward error-corrected logical qubits, while IBM\\\'s roadmap focuses on increasingly complex processors, modular architectures, quantum-classical integration, and fault-tolerant systems. Microsoft is pursuing a topological-qubit approach aimed at scalable quantum computing. This paper reviews the current state of quantum computing, examines major technological and scientific challenges, analyzes current research directions, and identifies future opportunities in drug discovery, materials science, optimization, cryptography, artificial intelligence, finance, and scientific simulation. The study concludes that the most realistic future is likely to involve quantum-centric computing, in which quantum processors work together with classical CPUs, GPUs, high-performance computing systems, and quantum networks rather than completely replacing classical computers.
Emerging as a breakthrough technology able to transform vast-scale healthcare data processing is quantum computing. For conventional computer methods, the complexity and volume of healthcare dataincluding medical records, genetic sequencing, and real-time patient monitoring datacause great difficulties. In medical research & clinical applications, quantum computingcapable of processing vast amounts of information concurrentlyis a reasonable choice for accelerated data analysis, improved prediction accuracy & better decision-making in general. Using quantum algorithms might improve the accuracy & the efficiency of treatments, disease diagnosis & the medication development as well as tailored approaches. Still, despite its promise, quantum computing is still in its early stage & faces several technical & pragmatic challenges. Hardware restrictions, error rates, and the need for specialized knowledge all limit their general usage. Moreover, particularly in the vital field of healthcare, protecting data security and privacy in quantum environments is of great relevance. While academics are assiduously searching for solutions to these issues, broad useful use is some years off. Still, ongoing development in quantum hardware, hybrid quantum-classical computer architectures, and algorithm improvement often helps new useful applications to arise. The future of quantum- enhanced healthcare solutions will depend on cooperation among lawmakers, computer scientists, and healthcare professionals. Advancing technology might allow revolutionary ideas that could alter the processing & use of healthcare data, therefore transforming medical systems & ultimately improving patient outcomes & efficiency of the medical systems. Future studies should focus on the correct integration of quantum computing into present healthcare systems by balancing theoretical promise with pragmatic concerns. Notwithstanding current constraints, the promise it offers makes this issue worthy of study as it might transform our knowledge and approach in healthcare all around
The transition from noisy intermediate-scale quantum (NISQ) processors to large-scale fault-tolerant machines requires more than incremental improvements in physical qubit fidelity. While advances in quantum error correction (QEC) theory continue to lower the theoretical overhead of quantum computing, much of this research remains abstracted from the physical realities of qubit implementations. This disconnect often results in hardware-agnostic designs that are inefficient or impractical to build. In this dissertation, I identify three primary gaps between theoretical QEC research and practical hardware: (1) the high levels of noise and temporal instability in experimental devices that impede scalable QEC; (2) the prohibitively large qubit and time costs of useful algorithms, driven largely by the limitations of the standard surface code; and (3) the failure of current resource estimates to account for hardware-level constraints, such as limited connectivity and the complexity of implementing non-local operations. To bridge these gaps, I propose a holistic, cross-stack approach where the error correction protocols and the physical hardware are designed in concert. By developing realistic device models and hardware-aware solutions at the lowest levels of the software stack, we can accelerate the path toward functional fault-tolerance. I present my work across three research thrusts: first, leveraging codesign to mitigate hardware-specific noise and reduce physical error rates; second, developing software tools for fault-tolerant quantum computers to reduce the space-time overhead of the QEC layer; and third, proposing two novel hardware-aware QEC architectures that provide practical, high-performance routes to scalable quantum computation.