Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times. In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated. Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.

翻译：云端可访问的量子处理器使得量子算法能够在异构硬件平台上直接执行。然而，与经典系统不同，由于量子比特连接性、门保真度和相干时间等架构差异，相同的量子电路在不同设备上可能表现出显著不同的行为。在本工作中，我们使用 Amazon Braket 平台，系统性地对五个代表性量子算法——贝尔态制备、GHZ 态生成、量子傅里叶变换、Grover 搜索算法和量子近似优化算法——在离子阱、超导以及模拟器后端上进行了基准测试。我们评估了包括保真度、CHSH 违背、成功概率、电路深度和门数量在内的性能指标。我们的结果表明，算法性能强烈依赖于硬件拓扑结构和噪声特性。例如，10 量子比特的 GHZ 态在离子阱硬件上实现了高于 0.8 的保真度，而由于路由开销和累积的双量子比特门误差，在超导平台上则降至 0.15 以下。这些发现凸显了硬件感知算法选择的重要性，并为 NISQ 时代的基准测试提供了实用指导。