Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the capability of the ansatzes and is seriously limited by optimization issues such as barren plateaus (i.e., vanishing gradients). This work proposes the state efficient ansatz (SEA) for accurate quantum dynamics simulations with improved trainability. First, we show that SEA can generate an arbitrary pure state with much fewer parameters than a universal ansatz, making it efficient for tasks like ground state estimation. It also has the flexibility in adjusting the entanglement of the prepared state, which could be applied to further improve the efficiency of simulating weak entanglement. Second, we show that SEA is not a unitary 2-design even if it has universal wavefunction expressibility and thus has great potential to improve the trainability by avoiding the zone of barren plateaus. We further investigate a plethora of examples in ground state estimation and notably obtain significant improvements in the variances of derivatives and the overall optimization behaviors. This result indicates that SEA can mitigate barren plateaus by sacrificing the redundant expressibility for the target problem.
翻译:预计变化量子算法(VQAs)将在近期量子计算机上建立有价值的应用。然而,最近的工程指出,VQAs的性能在很大程度上依赖于发射卫星的能力,并且严重受限于优化问题,如贫瘠高原(即渐变梯度)等优化问题。这项工作提议国家高效的 asatz (SEA) 进行精确量动态模拟,提高培训能力。首先,我们表明SEA可以产生一种任意的纯度,其参数比普世安萨茨少得多,使其对地面状态估计等任务具有效率。它还可以灵活调整所准备的状态的纠缠状态,这可以用来进一步提高模拟微弱纠缠现象的效率。第二,我们表明SEA不是一个统一的2级设计,即使它具有普遍的波变能力,因此具有通过避免贫瘠高地高原区而提高训练能力的巨大潜力。我们进一步调查了大量地面状态估计的例子,特别是使衍生物和总体优化行为水平的变异性有了显著的改进。这显示,SEA能够减轻高地的通货膨胀问题。