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Qtenon: Towards Low-Latency Architecture Integration for Accelerating Hybrid Quantum-Classical Computing

Chenning Tao, Liqiang Lu, Size Zheng, Li-Wen Chang, Minghua Shen, Hanyu Zhang, Fangxin Liu, Kaiwen Zhou, Jianwei Yin

2025Year
3Citations

Abstract

Hybrid quantum-classical algorithms have shown great promise in leveraging the computational potential of quantum systems.However, the efficiency of these algorithms is severely constrained by the limitations of current quantum hardware architectures.These architectures, which typically feature a decoupled design, lack both hardware support for low-latency communication and software support for fine-grained optimization.In this paper, we propose Qtenon, a tightly coupled system for efficient hybrid quantum-classical algorithm acceleration.Qtenon is composed of both hardware part and software part.To enable efficient communication and computation, the hardware part provides a unified memory hierarchy, an efficient quantum controller, as well as a multi-stage processing pipeline.The unified memory hierarchy functions as a communication buffer between host and quantum accelerators, with dedicated data paths and interfaces provided by the quantum controller.The multi-stage pipeline leverages hardware pipelines to fully exploit parallelism.To program hybrid quantum-classical algorithms on the hardware, our software part provides a set of instructions for data communication and computation.The instructions also enable fine-grained synchronization and efficient scheduling for quantum-host interaction.We design Qtenon as a RISC-V extended chip and implement it using Chisel.In evaluation, we achieve up to 14.9× end-to-end speedup compared to state-of-the-art work for hybrid quantum-classical algorithms.

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