论文标题
使用平滑脉冲和物理信息神经网络的稳健量子门
Robust quantum gates using smooth pulses and physics-informed neural networks
论文作者
论文摘要
量子计算机中的破坏性的存在需要抑制噪声。通过专门设计的控制脉冲动态校正的门提供了前进的路径，但是特定于硬件的实验限制可能会引起并发症。获得光滑脉冲的现有方法要么仅限于两级系统，因此需要优化噪声实现，或者仅限于分段连续脉冲序列。在这项工作中，我们提出了获得真正平滑脉冲的第一种通用方法，该方法可以最大程度地减少对噪声的敏感性，从而消除了对噪声实现的采样的需求，并就实验噪声的基本统计数据做出了假设。我们使用神经网络对哈密顿式进行参数，该网络允许使用大量优化参数来充分探索功能控制空间。我们通过找到平滑的形状来证明方法的能力，从而抑制逻辑子空间内噪声的影响以及从该子空间中泄漏。
The presence of decoherence in quantum computers necessitates the suppression of noise. Dynamically corrected gates via specially designed control pulses offer a path forward, but hardware-specific experimental constraints can cause complications. Existing methods to obtain smooth pulses are either restricted to two-level systems, require an optimization over noise realizations or limited to piecewise-continuous pulse sequences. In this work, we present the first general method for obtaining truly smooth pulses that minimizes sensitivity to noise, eliminating the need for sampling over noise realizations and making assumptions regarding the underlying statistics of the experimental noise. We parametrize the Hamiltonian using a neural network, which allows the use of a large number of optimization parameters to adequately explore the functional control space. We demonstrate the capability of our approach by finding smooth shapes which suppress the effects of noise within the logical subspace as well as leakage out of that subspace.
