论文标题
使用Clifford量子电路数据缓解错误
Error mitigation with Clifford quantum-circuit data
论文作者
论文摘要
尽管硬件噪声很大，但实现近期量子优势将需要准确估算量子可观察力。为此，我们提出了一种适用于基于门的量子计算机的新型，可扩展的误差方法。该方法生成培训数据$ \ {x_i^{\ text {noisy}}，x_i^{\ text {eckect {eckect}} \} $通过clifford Gates组成的量子电路，这些电路主要由Clifford Gates组成$ x_i^{\ text {Exact}} $分别是嘈杂的，无嘈杂的可观察结果。然后将线性ANSATZ拟合到此数据中，然后可以预测任意电路的无噪声可观察物。我们分析了我们的方法的性能与量子位，电路深度和非克利福德门的数量。我们在IBMQ Quantum计算机和64 Qubit的噪声模拟器上获得了16 QUIT的地面能量问题的降低顺序误差。
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.
