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Research Paper | Computer Science & Engineering | India | Volume 13 Issue 8, August 2024 | Rating: 5.2 / 10
Design and Implementation of a Novel Hybrid Quantum-Classical Processor for Enhanced Computation Speed
Mohammed Saleem Sultan [9] | Mohammed Shahid Sultan [9]
Abstract: 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. Results show 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.
Keywords: Quantum Computing, Hybrid Processor, QuantumClassical Integration, Optimization Algorithms, Quantum Chemistry
Edition: Volume 13 Issue 8, August 2024,
Pages: 1362 - 1373