Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{\text{noisy}}$ and $X_i^{\text{exact}}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

中文翻译：

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使用 Clifford 量子电路数据缓解错误

尽管存在显着的硬件噪声，但要实现近期的量子优势将需要准确估计量子可观测值。为此，我们提出了一种适用于基于门的量子计算机的新颖、可扩展的错误缓解方法。该方法通过主要由 Clifford 门组成的量子电路生成训练数据 $\{X_i^{\text{noisy}},X_i^{\text{exact}}\}$，可以有效地经典模拟，其中 $X_i^ {\text{noisy}}$ 和 $X_i^{\text{exact}}$ 分别是有噪声和无噪声的可观察值。将线性 ansatz 拟合到该数据然后允许预测任意电路的无噪声可观测值。我们分析了我们方法的性能与量子比特数、电路深度和非克利福德门数的关系。