Problem statement: In this study, a general plan of hybrid architecture for quantum algorithms is proposed. Approach: Analysis of the quantum algorithms shows that these algorithms were hybrid with two parts. First, the relationship of classical and quantum parts of the hybrid algorithms was extracted. Then a general plan of hybrid structure was designed. Results: This plan was illustrated the hybrid architecture and the relationship of classical and quantum parts of the algorithms. This general plan was used to increase implementation performance of quantum algorithms. Conclusion/Recommendations: Moreover, simulation results of quantum algorithms on the hybrid architecture proved that quantum algorithms can be implemented on the general plan as well.
Problem statement: In this study, a general plan of hybrid architecture for quantum algorithms is proposed. Approach: Analysis of the quantum algorithms shows that these algorithms were hybrid with two parts. First, the relationship of classical and quantum parts of the hybrid algorithms was extracted. Then a general plan of hybrid structure was designed. Results: This plan was illustrated the hybrid architecture and the relationship of classical and quantum parts of the algorithms. This general plan was used to increase implementation performance of quantum algorithms. Conclusion/Recommendations: Moreover, simulation results of quantum algorithms on the hybrid architecture proved that quantum algorithms can be implemented on the general plan as well.
The focus of this study is on developing a framework for a Quantum Algorithm Processing Unit (QAPU) with the hybrid architecture for classical-quantum algorithms. The framework is used to increase the implementation performance of quantum algorithms and design Quantum Processing Units (QPU). The framework shows a general plan for the architecture of quantum processors who is capable of run the quantum algorithms. In particular, the QAPU can be used as a quantum node to design a quantum multicomputer. At first, the hybrid architecture is designed for the quantum algorithms. Then, the relationship between the classical and the quantum part of hybrid algorithms is extracted, and main stages of the hybrid algorithm are developed. Next, the framework of the QAPU is designed. Furthermore, the framework is implemented and simulated for the existing quantum algorithms on a classic computer. It is shown that the framework is appropriate for quantum algorithms.
Abstract The capacity of quantum computing to tackle complex problems faster than general computers might lead to industry revolutions. However, actual implementation is problematic due to limited qubit coherence and inherent noise. Using Quantum Neural Networks (QNNs), hybrid quantum-classical algorithms successfully address optimization problems by combining the benefits of both quantum and traditional computer paradigms. The interface layer, the classical layer, and the quantum layer make up the three fundamental parts of the suggested design. The architecture's performance is contrasted with existing methods to demonstrate its advantages in terms of speed, accuracy, and scalability. With the help of this innovative design, difficult issues that are outside the capabilities of conventional computers can now be tackled, offering a workable solution for issues with finance, logistics, and medication development.
The theory of quantum complexity determines when quantum computers may offer a computational speed-up over classical computers. At present, there are only a few general well-known techniques in the field of quantum computing and finding the problems that are amenable to quantum speedups is a high priority. Nonetheless, efficient quantum algorithms are very limited in number and scope; no real breakthrough has yet been achieved in physical implementations. Most importantly, these algorithms are not still matured adequately to be applied in real quantum computations. The Quantum Processing Unit (QPU) is the processor of quantum computer that is able to do quantum computations. A typical component in QPU is a quantum device that runs quantum algorithms; namely Quantum Algorithm Processing Unit (QAPU). The focus of this study is developing a framework of QAPU and hybrid architecture for classical-quantum algorithms. The framework is used to increase the implementation performance of quantum algorithms. The framework shows a general plan for the architecture of quantum processor which is capable to run the quantum algorithms. In particular, QAPU can be used as a quantum node to design a quantum multicomputer. A uniform platform was proposed for the QAPU used in the present study. At first, the hybrid architecture was designed for the quantum algorithms. Then, the relationship between classical and quantum parts of the hybrid algorithms were extracted and the main stages of the hybrid algorithm were determined. Next, the framework of QAPU was designed and developed. For this purpose, some gates and connections were projected in the framework which could be applied for future quantum algorithms. Furthermore, the framework was set up, implemented and simulated for the existing quantum algorithms on a classic computer. The results were shown that the framework is appropriate for the quantum algorithms and had been mathematically proven. Moreover, a quantum algorithm was designed to solve the shortest paths problem between the two points in a network. Another quantum algorithm for a minimum weight spanning tree in the graph was also designed. These quantum algorithms can be applied to solve routing problem in the quantum networks and the quantum multicomputer. The designed hybrid architecture and framework were tested using the above quantum algorithms and the existing quantum algorithms. Implementing and simulating results are then represented based on the probabilistic hybrid algorithm in the case of linear array, binary heap and quantum search for dense and sparse graphs.
One of the primary focuses in the rapidly changing scenery of quantum computing is one of the most promising methodologies for transgressing the current feat of quantum technologies: a better mix of quantum and classical paradigms of computations.This paper suggests a new hybrid quantum-classical processor architecture that augments the computational speed and enhances efficiency by utilizing powers emanating from the quantum and classical processing units.The architecture proposed in the study incorporates quantum processing units with classical processing units, with CPUs interfaced tightly to support dynamic task allocation with a view toward computational requirements.Hybrid processor design caters to the performance of quantum algorithms that usually involve classical preprocessing and postprocessing, such as the Variational Quantum Eigensolver and Quantum Approximate Optimization Algorithm.These algorithms are essential for any complicated optimization, the simulation of quantum systems, and applications in machine learning.We provide an insightful processor architecture design, understanding the communication protocol details between QPU and CPU, the scheduling mechanism for hybrid tasks, and the optimization techniques employed to minimize data transfer overhead while maximizing computational throughput.A lot of these implementation challenges, like quantum error correction, managing the coherence time, and classical-quantum data conversion, are something that innovations in hardware design and software algorithms can achieve.In this paper, the performance of the hybrid processor is, for the first time, fully benchmarked based on simulation and experimental setups.Resultsshow that this hybrid approach outperforms standalone quantum and classical processors on specific computational tasks by ten times in optimization problems and five times for machine learning.These include case studies showing that for some problems, the processor can solve real-world problems much more efficiently than any existing quantum or classical system.Such case studies include quantum chemistry simulations, cryptographic tasks, and training machine learning models.We turn to the scalability and flexibility of the hybrid processor to extend the horizon of these case studies and to bridge the gap between the current quantum computing capabilities and the race toward fully functional quantum computers.This new hybrid quantum-classical processor architecture makes significant progress in quantum computing by providing a pragmatic, workable solution for harnessing quantum mechanics but using the maturity of classical computing.The results show how to proceed with the research involving the hybrid system and the many potential applications of such systems across optimization, cryptography, and artificial intelligence.This study is significant as it offers a practical approach to overcoming the limitations of current quantum and classical processors, providing a pathway to more efficient and scalable computational solutions.
We present a unified theoretical framework that classifies and analyzes quantum enhancement strategies for classical algorithms, establishing design paradigms that systematically combine quantum subroutines with classical procedures. The theory identifies four fundamental enhancement mechanisms: quantum search acceleration through amplitude amplification, quantum sampling for probabilistic optimization, quantum interference for solution space pruning, and quantum entanglement for correlation exploitation. We formalize each mechanism mathematically and derive general conditions under which they provide computational advantages, establishing a taxonomy of hybrid algorithm architectures. Our framework proves that quantum enhancement effectiveness depends critically on problem structure characteristics including solution density, constraint complexity, and objective function landscape topology. We establish theoretical bounds on achievable speedups for each enhancement paradigm and prove separation results showing that certain problem classes favor specific quantum integration strategies. The theory incorporates practical considerations including quantum circuit depth limitations, measurement overhead, and classical-quantum communication costs, providing realistic performance predictions. We extend the analysis to automated algorithm synthesis, deriving principles for selecting optimal quantum-classical decompositions given problem specifications and hardware constraints. This unified theoretical perspective advances quantum algorithm design from ad-hoc development to principled engineering, offering a comprehensive roadmap for creating hybrid quantum-classical algorithms across optimization, machine learning, simulation, and graph theory applications with provable performance characteristics.
Introduction: This article is the result of doctoral research conducted in 2025 on quantum software architecture, addressing the transformative potential of quantum computing and the challenges associated with integrating quantum capabilities into software systems. Problem: The lack of standardized architectural models for hybrid classical–quantum systems limits interoperability, reuse, and systematic software design. Objective: This study proposes a reference architecture to support the design and evolution of hybrid classical–quantum software systems. Methodology: The architecture was derived through a harmonization process. Results: The proposed framework defines ten architectural elements organized across three dimensions: Conceptual, Organizational, and Quality. Conclusion: The architecture provides a structured foundation for designing hybrid classical–quantum solutions. Originality: The work consolidates dispersed knowledge into a unified framework for hybrid classical–quantum systems. Limitations: The model is based on the currently limited body of literature and industrial experience, requiring future validation.