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Quantum Processing Framework And HybridAlgorithms For Routing Problems

Soltan Aghaei, Mohammad Reza

Published May 1, 2010• 0 citations
OpenAlex

Abstract

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.

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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.
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.
Quantum algorithms run on quantum computers are qualitatively different from those that run on classical computers. Quantum computing algorithms can be used for several problems in graph theory. Most of the classical algorithms involve searching over some space for finding the shortest-paths problem between two points in a graph and a minimal weight spanning tree. We modified classical Dijkstra's algorithm and implement quantum search instead of classical search, of which it will lead to more efficient algorithm. Also we proposed the structure for non-classical algorithms and design the various phases of the probabilistic quantum-classical algorithm for classical and quantum parts. Finally, we represent the result of implementing and simulating Dijkstra's algorithm as the probabilistic quantum-classical algorithm.
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.
Classical algorithms have been used to search over some space for finding the shortest paths problem between two points in a network and a minimal weight spanning tree for routing. Any classical algorithm deterministic or probabilistic will clearly used O(N) steps since on the average it will measure a large fraction of N records. Quantum algorithm is the fastest possible algorithm that can do several operations simultaneously due to their wave like properties. This wave gives an O( N ) steps quantum algorithm for identifying that record, where was used classical Dijkstra’s algorithm for finding shortest path problem in the graph of network and implement quantum search. Also we proposed the structure for non-classical algorithms and design the various phases of the probabilistic quantum-classical algorithm for classical and quantum parts. Finally, we represent the result of implementing and simulating Dijkstra's algorithm as the probabilistic quantum-classical algorithm.
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 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.