论文标题
统一的数据驱动量子错误缓解方法
Unified approach to data-driven quantum error mitigation
论文作者
论文摘要
实现近期量子优势将需要有效的方法来减轻硬件噪声。数据驱动的缓解错误方法是有希望的，其中包括零噪声外推（ZNE）和Clifford数据回归（CDR）在内的流行示例。在这里，我们提出了一种新颖的可扩展错误缓解方法，从概念上统一ZNE和CDR。我们的方法，称为可变的克利福德数据回归（VNCDR），在数值基准中显着优于这些单独的方法。 VNCDR首先通过近距离电路（可以模拟的）首先生成训练数据，其次是改变这些电路中的噪声水平。我们采用从IBM的我们的量子计算机获得的噪声模型来基准我们的方法。对于估计8 Q量ISING模型系统能量的问题，VNCDR将绝对能量误差提高了33倍，而ZNE和CDR的因素分别为20和1.8。对于用64吨的随机量子电路纠正可观察到的问题，VNCDR分别在ZNE和CDR上分别通过2.7和1.5的因素来改善误差。
Achieving near-term quantum advantage will require effective methods for mitigating hardware noise. Data-driven approaches to error mitigation are promising, with popular examples including zero-noise extrapolation (ZNE) and Clifford data regression (CDR). Here we propose a novel, scalable error mitigation method that conceptually unifies ZNE and CDR. Our approach, called variable-noise Clifford data regression (vnCDR), significantly outperforms these individual methods in numerical benchmarks. vnCDR generates training data first via near-Clifford circuits (which are classically simulable) and second by varying the noise levels in these circuits. We employ a noise model obtained from IBM's Ourense quantum computer to benchmark our method. For the problem of estimating the energy of an 8-qubit Ising model system, vnCDR improves the absolute energy error by a factor of 33 over the unmitigated results and by factors 20 and 1.8 over ZNE and CDR, respectively. For the problem of correcting observables from random quantum circuits with 64 qubits, vnCDR improves the error by factors of 2.7 and 1.5 over ZNE and CDR, respectively.
