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 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 of 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.

中文翻译：

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数据驱动量子错误缓解的统一方法

实现近期量子优势将需要有效的方法来减轻硬件噪声。数据驱动的错误缓解方法很有前景，流行的例子包括零噪声外推 (ZNE) 和克利福德数据回归 (CDR)。在这里，我们提出了一种可扩展的错误缓解方法，该方法在概念上统一了 ZNE 和 CDR。我们的方法称为可变噪声 Clifford 数据回归 (vnCDR)，在数值基准测试中明显优于这些单独的方法。vnCDR 首先通过近克利福德电路（经典可模拟）生成训练数据，然后通过改变这些电路中的噪声水平来生成训练数据。我们使用从 IBM 的 Ourense 量子计算机获得的噪声模型来对我们的方法进行基准测试。对于估计 8-qubit Ising 模型系统的能量的问题，vnCDR 将绝对能量误差比未减轻的结果提高了 33 倍，比 ZNE 和 CDR 分别提高了 20 和 1.8 倍。对于从具有 64 个量子位的随机量子电路中校正可观察量的问题，vnCDR 将误差分别比 ZNE 和 CDR 提高了 2.7 和 1.5 倍。