论文标题
近期量子设备上噪声预测的深度学习模型
A deep learning model for noise prediction on near-term quantum devices
论文作者
论文摘要
我们提出了一种量子电路的深度学习编译器的方法，该方法旨在减少在特定设备上运行的电路的输出噪声。我们从量子设备上的实验数据上训练卷积神经网络，以学习特定于硬件的噪声模型。然后，编译器将训练有素的网络用作噪声预测指标，并在电路中插入大门序列，以最大程度地减少预期噪声。我们在IBM 5 QUIT设备上测试了这种方法，并观察到与Qiskit Compiler获得的电路相比，输出噪声降低了12.3％（95％CI [11.5％，13.0％]）。此外，受过训练的噪声模型是特定于硬件的：在另一台设备上训练的噪声模型只能减少5.2％（95％CI [4.9％，5.6％]）。这些结果表明，使用机器学习的设备特异性编译器可能会产生更高的保真度操作，并为噪声模型设计提供见解。
We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise. We tested this approach on the IBM 5-qubit devices and observed a reduction in output noise of 12.3% (95% CI [11.5%, 13.0%]) compared to the circuits obtained by the Qiskit compiler. Moreover, the trained noise model is hardware-specific: applying a noise model trained on one device to another device yields a noise reduction of only 5.2% (95% CI [4.9%, 5.6%]). These results suggest that device-specific compilers using machine learning may yield higher fidelity operations and provide insights for the design of noise models.
