What else than Hybrid Quantum Computing will dominate data centers, just within a decade? But until now, it is unclear how precisely such high-performance computers will be constructed, even the fundamental functionality is in question. Thus, it is very hard to prepare today, from a programmer perspective, in-depth for the anticipated near-term programming paradigm shift due to quantum computers. Furthermore, the industry is reluctant to invest heavily into quantum software development right now, since under this uncertainty regarding the future development of hardware and Cloud services, no one would like to see all these efforts to be ruined by a novel technology, an unforeseen new machine, which would eradicate the software in place, which the industry had developed so costly. This calls for a more generic view on Hybrid Quantum Computing, on one hand hardware agnostic, but still anticipating the fundamental laws of nature which rule any future quantum computing system, regardless of its engineered excellence. This generalization can be done with the introduction of a Virtual Quantum Processor, a piece of imaginary hardware, which is described detailed enough, to emulate a generic hybrid quantum machine based on a set of instructions within a Turing machine. If this could be accomplished, we would retrieve a Uniform Computing Model for Hybrid Quantum Software, which can be applied later to any physical representation of quantum computing hardware, but would run already today on our current machines.
What else than Hybrid Quantum Computing will dominate data centers, just within a decade? But until now, it is unclear how precisely such high-performance computers will be constructed, even the fundamental functionality is in question. Thus, it is very hard to prepare today, from a programmer perspective, in-depth for the anticipated near-term programming paradigm shift due to quantum computers. Furthermore, the industry is reluctant to invest heavily into quantum software development right now, since under this uncertainty regarding the future development of hardware and Cloud services, no one would like to see all these efforts to be ruined by a novel technology, an unforeseen new machine, which would eradicate the software in place, which the industry had developed so costly. This calls for a more generic view on Hybrid Quantum Computing, on one hand hardware agnostic, but still anticipating the fundamental laws of nature which rule any future quantum computing system, regardless of its engineered excellence. This generalization can be done with the introduction of a Virtual Quantum Processor, a piece of imaginary hardware, which is described detailed enough, to emulate a generic hybrid quantum machine based on a set of instructions within a Turing machine. If this could be accomplished, we would retrieve a Uniform Computing Model for Hybrid Quantum Software, which can be applied later to any physical representation of quantum computing hardware, but would run already today on our current machines.
This is article examines the issue of new conceptual architectures of ultrafast hybrid computing devices based on digital technologies, where the size of an elementary computing device approaches the size of a molecule or even an atom. At this level, the laws of classical physics stop working and quantum laws begin to operate, which for many important dynamic problems have yet to be described theoretically. For such practically significant examples of the use of quantum computers as the training of language models, as well as the analysis of the effectiveness of model training on specified chips and with specified train characteristics (model depth, dataset size, etc.), a new concept for the use of hybrid computing platforms in data centers has emerged. Hybrid computing platforms for data centers represent the integration of various computing resources, including on-premises servers, cloud solutions and virtualized environments. These platforms allow organizations to optimize their computing power, providing flexibility, scalability and costeffectiveness. With the increase in data volumes and the need for cloud services, there is an increase in the creation of data centers. The edge-computing extension, which supports real-time processing at data collection points, matches the potential of quantum computing for revolutionary tasks such as optimization and decision-making. As peripheral systems evolve, the integration of algorithms with quantum technologies can expand their capabilities. This raises pitfalls in evaluating processor performance in hybrid computers, which can vary depending on many factors, including the processor architecture, the types of cores used (for example, high-performance and energy-efficient), as well as the nature of the tasks performed. In this study, a mathematical model of the presented systems is proposed as a combination of combining various concepts that will help to understand and evaluate the performance of hybrid computing, allowing you to optimize the use of resources and improve overall efficiency. Today, there is a growing dependence on hybrid models - mixing local, cloud and peripheral systems, which further emphasizes the need for an infrastructure that accommodates both classical and quantum resources. Quantum computing is increasingly seen as an important part of these hybrid architectures, preparing organizations to meet the new challenges of the advanced computing landscape.
Abstract Quantum computing leverages fundamental quantum-mechanical phenomena—most notably superposition, entanglement, and quantum tunneling—to perform computations in ways that differ fundamentally from classical digital processing. Compared to conventional computing architectures, quantum systems promise significant reductions in computational time and potentially lower energy consumption for specific classes of problems. These advantages, combined with advances in nanoscale engineering and the slowing of traditional hardware scaling trends such as Moore’s Law, have led to substantial commercial and national-security-driven investments in quantum technologies over the past decade. A major milestone was reached when Google announced the experimental demonstration of “quantum supremacy,” showing that a quantum processor could execute a specific computational task infeasible for a classical supercomputer within practical time constraints. Although the demonstrated problem had no immediate practical application, this achievement marked a crucial step toward the development of quantum systems capable of delivering real-world computational advantages and provided a more precise technical framing of the concept of quantum advantage. Despite the fact that the mathematical foundations, programming models, and principal quantum algorithms were established in the 1990s, quantum computing remained largely confined to a specialized research community for decades. Today, the increasing accessibility of cloud-based quantum hardware calls for a broader dissemination of implementation-oriented knowledge among researchers and practitioners in computer science, software engineering, and related fields. The quantum programming paradigm differs fundamentally from classical programming models and often relies on physical and algebraic notations that may pose barriers to traditionally trained computer scientists. This series of articles provides a self-contained and concise introduction to quantum computing and its core algorithms, with a strong emphasis on practical implementation. Given that real quantum processors—such as those accessible via the IBM quantum ecosystem—are now available as cloud services, experimental validation can be conducted both on simulators and on actual hardware for small-scale inputs. While other platforms such as Rigetti and IonQ also offer cloud-based quantum computing services, this review focuses exclusively on the IBM quantum computing ecosystem.
The topic of this dissertation stands at the crossroads of two emerging technologies: highly-parallel computing and quantum computing. Both seek to go beyond the limits of current computing practice: in the absence of further miniaturization, highly-parallel computing seeks to increase the available computer power substantially. Quantum Computing looks beyond the classical approach to computation, leveraging quantum-mechanical effects to increase computing power in a fundamental way. In the absence and also in the presence of actual quantum computers it is crucial to support the development of quantum computer applications by creating an appropriate software framework. Creating such a framework currently faces two major research challenges. First, one needs to separate the realization of the quantum computer from the software running on it, whether it is an actual physical or a simulated realization. Our first contribution lies with defining a layered software architecture, ranging from a design tool at the higher abstraction levels to a simulated execution layer underneath. At its core sits a ‘Quantum Virtual Machine’ using the Measurement Calculus as the underlying quantum computational model. Such a virtual machine layer makes the application developed on top of it oblivious to how it is executed. The simulated execution of this quantum virtual machine is fundamentally a computationally intensive task. Parallel computing offers a way to throw more computational resources at the problem, in the light of the saturation of sequential performance of current computers. Simulation is the domain of the second challenge, as it has very high performance requirements. The simulation problem thus becomes: how does one expose the inherent parallelism of quantum computing simulation in a fundamental way, so as to be usable for the highly-parallel computers to come? Our second contribution is to provide a formal translation of quantum programs to highly-parallel dataflow computations, and to develop a virtual environment that compiles and executes such programs. This work demonstrates several properties of both the formal as well as the virtual level of computation that validate our approach.
Quantum computers can often be used in a hybrid setting, where classical computers still play an important role in the operation and use of the quantum computer. A hybrid architecture can serve as a foundational paradigm for realizing the early potential of quantum technologies, allowing us to combine the advantages of classical systems with the emerging capabilities of quantum processors to enable useful computations despite hardware limitations. Keeping the Turing machine for the classical part, this paper provides 5 types of quantum computational models and investigates the key effects and challenges of each from a theoretical point of view. In addition, it gives an overview of the key properties and recent advances to integrate the most efficient and feasible hybrid scheme based on the application in our Quantum Network.
Quantum computing, built on the principles of quantum mechanics, has emerged as a transformative paradigm with the potential to revolutionize computational capabilities, enabling solutions to problems that are currently intractable for classical systems. Quantum computing integrates hardware, architectures, and software for improved applications while utilizing superposition, entanglement, and interference to promote materials science, cryptography, optimization, and artificial intelligence. Sections introduce and describe the model components needed in order to create quantum processors, including qubits, quantum gates and control electronics, as well as a review of new developments in quantum software frameworks and programming tools. Notable examples, such as Shor’s algorithm for cryptographic caveat, Grover’s algorithm for inefficient search processes, and quantum-enhanced optimization, illuminate the position of quantum computing as a means of tackling tough problems that span disciplines, such as pharmaceuticals, predictors of climate, and finance modeling. However, the field continues to face significant challenges, like limited scalability, quantum gate error rates, qubit coherence times, and difficulties integrating with classical systems. Future efforts will focus on developing hybrid quantum-classical systems, scalable architectures, and industry- specific applications to transition quantum computing from exploration to practical deployment. This review provides a comprehensive analysis of quantum computing, covering its potential, trends, and challenges, while exploring hardware-software integration, classicalquantum comparisons, and insights to guide research and industry in unlocking its revolutionary possibility.
Quantum computing represents a transformative approach to computation leveraging fundamental principles of quantum mechanics such as superposition, entanglement and interference to store and process information in ways that classical computers cannot achieve.This paper attempts to provide a bona fide bibliographic overview of the principles, computational models, hardware technologies, and potential applications of quantum computing.It analyses how quantum algorithms could outperform classical algorithms in cryptographic analysis, optimization, and big data analysis.The survey integrates viewpoints from recent works; it presents system architectures, software development challenges, and quantum-assisted applications in healthcare, artificial intelligence, cybersecurity, and industrial optimization.Meanwhile, it examines hybrid quantum-classical systems that would allow quantum technologies to be deployed in the near term skirted by the limitations currently posed by quantum hardware.Moreover, the paper discusses foremost hurdles in scaling, error-correction, and maintaining coherence.From reviewing the recent developments and whether possible, this present review work informs researchers, engineers, and practitioners alike on the current state, as well as on the long-run viability, of quantum computing.