Variational hybrid quantum-classical optimization represents one of the most
promising avenue to show the advantage of nowadays noisy intermediate-scale
quantum computers in solving hard problems, such as finding the minimum-energy
state of a Hamiltonian or solving some machine-learning tasks. In these devices
noise is unavoidable and impossible to error-correct, yet its role in the
optimization process is not well understood, especially from the theoretical
viewpoint. Here we consider a minimization problem with respect to a
variational state, iteratively obtained via a parametric quantum circuit,
taking into account both the role of noise and the stochastic nature of quantum
measurement outcomes. We show that the accuracy of the result obtained for a
fixed number of iterations is bounded by a quantity related to the Quantum
Fisher Information of the variational state. Using this bound, we study the
convergence property of the quantum approximate optimization algorithm under
realistic noise models, showing the robustness of the algorithm against
different noise strengths.

采用参数量子电路迭代地获得变分态，考虑噪音和量子测量结果的随机性，使用与变分态相关的量子费舍尔信息界限定结果精度，并通过实验展示该算法在不同噪声强度下的鲁棒性。

具噪声鲁棒性的变分混合量子 - 经典优化

Noise-Resilient Variational Hybrid Quantum-Classical Optimization

We propose to use the quantum Fisher information in characterizing the
information flow of open quantum systems. This information-theoretic approach
provides a quantitative measure to statistically distinguish Markovian and
non-Markovian processes. A basic relation between the QFI flow and
non-Markovianity is unveiled for quantum dynamics of open systems. For a class
of time-local master equations, the exactly-analytic solution shows that the
non-Markovianity is characterized by additive information sub-flows in
different non-Markovian channels.

该研究提出使用量子费舍尔信息来表征开放式量子系统中的信息流，并揭示了 QFI 流与非马尔可夫性之间的基本关系，为一类时间局部主方程提供了确切的解析解并表明非马尔可夫性由不同非马尔可夫通道中的附加信息子流表征。