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arXiv 2005.10811quant-ph
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用于近期量子设备噪声预测的深度学习模型

A deep learning model for noise prediction on near-term quantum devices

Alexander Zlokapa, Alexandru Gheorghiu

摘要:

我们提出了一种用于量子线路的深度学习编译器的方法,旨在降低在特定设备上运行的电路的输出噪声。我们在来自量子设备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,编译器使用训练的网络作为噪声预测器,并在电路中插入门序列,以使预期的噪声最小化。我们在IBM 5量子位设备上测试了这种方法,并观察到与Qiskit编译器获得的电路相比,输出噪声降低了12.3%(95%CI [11.5%,13.0%])。此外,经过训练的噪声模型是特定于硬件的:将在一个设备上训练的噪声模型应用于另一个设备,噪声降低仅为5.2%(95% CI [4.9%,5.6%])。这些结果表明,使用机器学习的特定于设备的编译器可以产生更高保真的操作,并为噪声模型的设计提供见解。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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