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：实现量子系统上的高性能计算是一项严峻挑战，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，该架构融合了前沿量子软件框架与高性能经典计算资源，旨在解决材料与凝聚态物理领域量子模拟中的难题。该架构的核心在于：在量子处理单元（QPU）上高效运行VQE算法进行量子态制备，同时利用张量网络态和量子卷积神经网络（QCNN）在经典硬件上实现量子态分类。为基准测试量子模拟器，QCQ架构借助cuQuantum SDK实现多GPU加速，并集成PennyLane的Lightning插件，在复杂相变分类任务中相比传统CPU方法实现了高达十倍的计算速度提升。这一显著加速使得横场伊辛模型和XXZ模型等系统能够以99.5%的准确率精准预测相变。该架构通过在QPU与经典资源间分配计算任务，有效破解了量子-HPC中的关键瓶颈，为可扩展量子模拟铺平了道路。QCQ框架体现了量子算法、机器学习与量子-HPC能力的协同融合，极大增强了其跨尺度揭示量子系统行为变革性洞察的潜力。随着量子硬件的持续进步，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最先进经典计算基础设施，在充分发挥量子计算潜能中扮演关键角色。