标题： 超导量子比特的加速噪声光谱分析 显示英文标题

标题： Expedited Noise Spectroscopy of Transmon Qubits

摘要： 近年来，量子计算硬件的物理实现取得了巨大进展，使我们比以往任何时候都更接近实现量子计算的承诺。 然而，在扩大当今量子处理器规模时，噪声仍然是一个关键挑战。 虽然退相干限制了量子比特在存在不可控噪声源的情况下长时间存储信息的能力，但状态制备和测量控制方法的错误实施会导致量子电路的故障实现。 传统的噪声光谱协议可以表征和建模环境噪声，但通常资源消耗大且耗时。 此外，底层噪声的性质可能随时间变化，使得噪声轮廓提取变得无用，因为这种新信息无法被用来改进量子纠错或动态解耦协议。 在本工作中，我们使用基于机器学习的方法快速提取多个量子比特的噪声光谱，并展示了一种可能的噪声缓解策略。 该过程包括实施低要求的动态解耦序列以记录被研究量子比特的相干衰减，然后借助在合成数据集上预训练的卷积神经网络预测底层噪声光谱。 尽管我们的协议几乎与硬件无关，但我们使用IBM量子平台上可用的超导量子比特验证了其有效性。 我们进一步利用这些快速获得但准确的噪声光谱来设计定制的动态解耦序列并进行时间依赖的噪声光谱分析。 显示英文摘要

摘要： There has been tremendous progress in the physical realization of quantum computing hardware in recent times, bringing us closer than ever before to realizing the promise of quantum computing. However, noise continues to pose a crucial challenge when it comes to scaling up present day quantum processors. While decoherence limits the qubits ability to store information for long periods in the presence of uncontrollable noise sources, the erroneous implementation of control methods for state preparation and measurements leads to faulty implementations of quantum circuits. Conventional noise spectroscopy protocols can characterize and model environmental noise but are usually resource intensive and lengthy. Moreover, the underlying noise can vary in nature over time, making noise profile extraction futile as this new information cannot be harnessed to improve quantum error correction or dynamical decoupling protocols. In this work, we address this challenge using a machine learning-based methodology to quickly extract noise spectra of multiple qubits and demonstrate a possible noise mitigation strategy. The procedure involves implementing undemanding dynamical decoupling sequences to record coherence decays of the investigated qubits and then predict the underlying noise spectra with the help of a convolution neural network pre-trained on a synthetic dataset. While our protocol is virtually hardware-agnostic, we validate its effectiveness using superconducting qubits available on the IBM Quantum platform. We further use these rapidly obtained, yet accurate, noise spectra to design bespoke dynamic decoupling sequences and perform time-dependent noise spectroscopy.