实践中的量子误差管理：跨栈基准测试
Quantum Error Management in Practice: A Cross-Stack Benchmark
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中文总结 AI 辅助
该研究在IBM 156量子比特处理器上对三种量子误差管理商业方案进行跨栈基准测试，发现QESEM和Q-CTRL可大幅降低误差，但二者在精度与QPU时间上存在不同权衡。
中文摘要 AI 辅助
量子处理器已突破100量子比特大关，但噪声仍限制着电路性能，而完整的量子纠错对于常规使用来说成本过高。因此，误差抑制与缓解在从当前硬件中获取价值方面发挥着重要作用，然而在相同工作负载和设备上对商业解决方案进行独立比较的研究仍然稀缺。我们在IBM Pittsburgh（一款156量子比特的IBM Quantum Heron r3处理器）上对IBM Qiskit Runtime、Q-CTRL Performance Management和Qedma QESEM进行基准测试。对于Sampler工作负载，我们运行Bernstein-Vazirani算法、量子相位估计、GHZ态制备以及最多包含100个测量量子比特的随机镜像电路，比较原始执行、IBM测量旋转和Q-CTRL的表现。对于Estimator工作负载，我们针对25、50和75量子比特的8层横场伊辛电路，测量链平均磁化强度和关联可观测量，并与精确矩阵乘积态参考值进行对比，比较IBM原始执行、IBM TREX加旋转、Q-CTRL和QESEM的表现。Q-CTRL在三种结构化Sampler工作负载中取得了最佳结果，同时将报告的QPU时间保持在与IBM配置相同的数量级内。在6种伊辛可观测量和系统规模案例中，IBM原始执行的总平均绝对误差为0.0883，IBM TREX加旋转为0.0807，Q-CTRL为0.0285，QESEM为0.0188。与原始执行相比，Q-CTRL和QESEM分别将总误差降低了3.10倍和4.70倍，而QESEM使用的报告QPU时间是Q-CTRL的7.5至11.1倍。这些结果表明，受管理的误差抑制与缓解可以显著提升当前硬件性能，但存在明显的精度与执行时间权衡。
英文摘要
Quantum processors have crossed the one-hundred-qubit mark, but noise continues to limit circuit performance, while full quantum error correction remains too costly for routine use. Error suppression and mitigation therefore play an important role in extracting value from current hardware, yet independent comparisons of commercial solutions on identical workloads and devices remain scarce. We benchmark IBM Qiskit Runtime, Q-CTRL Performance Management, and Qedma QESEM on IBM Pittsburgh, a 156-qubit IBM Quantum Heron r3 processor. For Sampler workloads, we run Bernstein-Vazirani, quantum phase estimation, GHZ-state preparation, and randomized mirror circuits with up to 100 measured qubits, comparing raw execution, IBM measurement twirling, and Q-CTRL. For Estimator workloads, we measure chain-averaged magnetization and correlation observables of an eight-layer transverse-field Ising circuit at 25, 50, and 75 qubits against an exact matrix-product-state reference, comparing IBM raw execution, IBM TREX plus twirling, Q-CTRL, and QESEM. Q-CTRL produced the best results on the three structured Sampler workloads while keeping reported QPU times within the same order as the IBM configurations. Across six Ising observable and system-size cases, aggregate mean absolute error was 0.0883 for IBM raw execution, 0.0807 for IBM TREX plus twirling, 0.0285 for Q-CTRL, and 0.0188 for QESEM. Relative to raw execution, Q-CTRL and QESEM reduced aggregate error by factors of 3.10 and 4.70, respectively, while QESEM used 7.5 to 11.1 times the reported QPU time of Q-CTRL. These results show that managed error suppression and mitigation can substantially improve current hardware performance, but with distinct accuracy and execution-time tradeoffs.
