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.
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.
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.
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.
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.
Quantum computing stands at a transformative threshold, with error-correcting quantum processors expected to emerge within the next five years. This technological advancement will be crucial for maintaining competitive advantage in fields such as cryptography, optimization, molecular medicine, and image processing, with mainstream quantum computing projected by 2030. This study introduces a novel Quantum Parallel Framework (QPF) designed to prepare for the integration of quantum computing in industrial applications. The research focuses on developing and implementing parallel quantum algorithms for practical commercial applications, particularly in image processing. The QPF provides a comprehensive methodology for organizations to build expertise in quantum computing integration with classical systems. It includes a Qiskit interface for managing quantum algorithms and communications with Quantum Processing Units (QPUs), along with a C++ and OpenGL-based front end. The framework's capabilities are demonstrated through a case study implementing a Quantum Hadamard Edge Detector (QHED) in parallel execution. The study evaluates various performance parameters, including QHED threads, processors, Non-Uniform Memory Allocation (NUMA) strategy, thread priority, and image resolution, to optimize processing efficiency. The framework provides a practical pathway for organizations to develop and mature their quantum computing capabilities, focusing on real-world applications and system integration. This work contributes to the growing field of quantum computing by offering a methodology that can be adapted for various quantum algorithms and applications, particularly in preparation for more powerful self-error correcting QPUs.
The convergence of quantum computing and artificial intelligence represents a paradigm shift in computational capability, enabling solutions to previously intractable optimization problems, accelerated machine learning training, and enhanced natural language understanding through quantum state exploitation. This paper presents a comprehensive framework encompassing five distinct quantum artificial intelligence architectures: Quantum Machine Learning utilizing quantum circuit-based training and inference, Quantum-Inspired AI implementing quantum algorithmic principles on classical hardware, Hybrid Quantum-Classical AI leveraging collaborative processing between central processing units and quantum processing units, Quantum Optimization AI for solving complex combinatorial problems, and Quantum Natural Language Processing exploiting quantum superposition for semantic reasoning. The quantum machine learning architecture implements variational quantum eigensolvers and quantum kernel methods for feature space transformation, achieving exponential speedup in specific classification tasks. The quantum-inspired approach applies tensor network decomposition and quantum annealing simulation on classical systems, demonstrating polynomial speedup for optimization problems. The hybrid quantum-classical framework orchestrates workload partitioning between conventional processors and quantum accelerators through dynamic task allocation algorithms, optimizing for quantum circuit depth and classical preprocessing overhead. Quantum optimization leverages quantum annealing and quantum approximate optimization algorithms for solving large-scale combinatorial problems in logistics, finance, and molecular simulation. Quantum natural language processing implements quantum word embeddings in Hilbert space, enabling superposition-based semantic analysis with logarithmic dimensional complexity compared to classical word vector representations. This research establishes theoretical foundations and architectural blueprints for quantum-enhanced artificial intelligence systems, positioning quantum computing as a transformative technology for next-generation machine learning, optimization, and cognitive computing applications.
Quantum computers have demonstrated utility in simulating quantum systems beyond brute-force classical approaches. As the community builds on these demonstrations to explore using quantum computing for applied research, algorithms and workflows have emerged that require leveraging both quantum computers and classical high-performance computing (HPC) systems to scale applications, especially in chemistry and materials, beyond what either system can simulate alone. Today, these disparate systems operate in isolation, forcing users to manually orchestrate workloads, coordinate job scheduling, and transfer data between systems -- a cumbersome process that hinders productivity and severely limits rapid algorithmic exploration. These challenges motivate the need for flexible and high-performance Quantum-Centric Supercomputing (QCSC) systems that integrate Quantum Processing Units (QPUs), Graphics Processing Units (GPUs), and Central Processing Units (CPUs) to accelerate discovery of such algorithms across applications. These systems will be co-designed across quantum and classical HPC infrastructure, middleware, and application layers to accelerate the adoption of quantum computing for solving critical computational problems. We envision QCSC evolution through three distinct phases: (1) quantum systems as specialized compute offload engines within existing HPC complexes; (2) heterogeneous quantum and classical HPC systems coupled through advanced middleware, enabling seamless execution of hybrid quantum-classical algorithms; and (3) fully co-designed heterogeneous quantum-HPC systems for hybrid computational workflows. This article presents a reference architecture and roadmap for these QCSC systems.