论文标题
评估变异量子算法的噪声弹性
Evaluating the noise resilience of variational quantum algorithms
论文作者
论文摘要
我们模拟了变异量子算法的状态制备电路中不同类型的噪声的影响。我们首先使用差异量子质量器在存在噪声的情况下找到哈密顿量的基态，并采用两种质量措施，即能量，即保真度和同意。然后，我们将任务扩展到构造的一个，并使用一组一般的随机目标状态构造任务。我们确定了不同类型和噪声水平的最佳电路深度，并观察到通过适应优化参数来减轻噪声的影响。我们发现，包含冗余的参数化门使量子电路对噪声更具弹性。对于这种过度参数的电路，不同的参数集可能会导致相同的最终状态，在无噪声情况下，我们将其表示为参数退化。从数字上讲，我们表明可以在存在噪声的情况下取消这种退化性，而某些状态比其他状态更适合噪声。我们还表明，与依赖电路依赖性阈值相比，与目标状态的平均偏差在噪声水平上是线性的。在该区域中，偏差由随机模型很好地描述。在阈值之上，优化可以收敛到与真实目标状态具有很大不同物理特性的状态，因此对于实际应用，确保噪声水平低于此阈值至关重要。
We simulate the effects of different types of noise in state preparation circuits of variational quantum algorithms. We first use a variational quantum eigensolver to find the ground state of a Hamiltonian in presence of noise, and adopt two quality measures in addition to the energy, namely fidelity and concurrence. We then extend the task to the one of constructing, with a layered quantum circuit ansatz, a set of general random target states. We determine the optimal circuit depth for different types and levels of noise, and observe that the variational algorithms mitigate the effects of noise by adapting the optimised parameters. We find that the inclusion of redundant parameterised gates makes the quantum circuits more resilient to noise. For such overparameterised circuits different sets of parameters can result in the same final state in the noiseless case, which we denote as parameter degeneracy. Numerically, we show that this degeneracy can be lifted in the presence of noise, with some states being significantly more resilient to noise than others. We also show that the average deviation from the target state is linear in the noise level, as long as this is small compared to a circuit-dependent threshold. In this region the deviation is well described by a stochastic model. Above the threshold, the optimisation can converge to states with largely different physical properties from the true target state, so that for practical applications it is critical to ensure that noise levels are below this threshold.
