论文标题
通过隐藏的倒置量子的量子误差在超导量子设备中的协议缓解
Quantum error mitigation by hidden inverses protocol in superconducting quantum devices
论文作者
论文摘要
我们提出了一种基于隐藏的倒置来改善变异算法的收敛的方法，以减轻相干错误。在缓解错误的背景下，这意味着用其倒置代替某些Hermitian大门的硬件实现。这样做会导致消除噪声和更弹性的量子电路。这种方法改善了各种两量误差模型的性能，噪声运算符也随着栅极反转而反转。我们将缓解方案应用于运行各种量子本元素（VQE）算法的超导量子处理器，以找到H $ _ {\ rm 2} $地基态能量。在超导硬件上实施时，我们发现缓解方案有效地减少了VQE中参数学习路径中的能量波动，从而减少了融合值的迭代次数。我们还提供了不同噪声模型下VQE性能的详细数值模拟，并探讨了隐藏的逆向\和随机编译如何影响学习问题的潜在损失格局。这些仿真有助于解释我们的实验硬件结果，有助于将低级门的性能与特定于应用程序的行为联系起来，而与富达之类的指标相比，通常无法为观察到的高级性能提供直观的见解。
We present a method to improve the convergence of variational algorithms based on hidden inverses to mitigate coherent errors. In the context of error mitigation, this means replacing the on hardware implementation of certain Hermitian gates with their inverses. Doing so results in noise cancellation and a more resilient quantum circuit. This approach improves performance in a variety of two-qubit error models where the noise operator also inverts with the gate inversion. We apply the mitigation scheme on superconducting quantum processors running the variational quantum eigensolver (VQE) algorithm to find the H$_{\rm 2}$ ground-state energy. When implemented on superconducting hardware we find that the mitigation scheme effectively reduces the energy fluctuations in the parameter learning path in VQE, reducing the number of iterations for a converged value. We also provide a detailed numerical simulation of VQE performance under different noise models and explore how hidden inverses \& randomized compiling affect the underlying loss landscape of the learning problem. These simulations help explain our experimental hardware outcomes, helping to connect lower-level gate performance to application-specific behavior in contrast to metrics like fidelity which often do not provide an intuitive insight into observed high-level performance.
