Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software framework works with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics. At the heart of this architecture is the seamless integration of VQE algorithms running on QPUs for efficient quantum state preparation, Tensor Network states, and QCNNs for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane's Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture's ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in Quantum-HPC, paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure.

翻译：在量子系统上实现高性能计算面临巨大挑战，亟需弥合量子硬件与经典计算资源之间的能力鸿沟。本研究提出了一种创新的分布式感知量子-经典-量子（QCQ）架构，该架构将前沿量子软件框架与高性能经典计算资源相结合，以应对材料科学与凝聚态物理领域量子模拟的挑战。该架构的核心在于：在量子处理器（QPU）上运行变分量子本征求解器（VQE）算法以实现高效的量子态制备，同时集成张量网络态与量子卷积神经网络（QCNN），在经典硬件上完成量子态分类任务。为对量子模拟器进行基准测试，QCQ架构利用cuQuantum开发工具包，结合PennyLane的Lightning插件实现多GPU加速，在处理复杂相变分类任务时，相比传统CPU方法实现了高达十倍的运算速度提升。这一显著加速使横场伊辛模型和XXZ模型等系统能够以99.5%的准确率精准预测相变。该架构在QPU与经典资源之间进行分布式计算的能力，有效解决了量子-高性能计算中的关键瓶颈问题，为可扩展量子模拟铺平了道路。QCQ框架实现了量子算法、机器学习与量子-高性能计算能力的协同融合，极大增强了其从不同尺度揭示量子系统行为变革性认知的潜力。随着量子硬件的持续演进，这种混合分布式感知框架将通过无缝整合分布式量子资源与尖端经典计算基础设施，在发挥量子计算全部潜力方面发挥关键作用。