Statistical analysis of barren plateaus in variational quantum algorithms

Abstract

We investigate the barren plateau (BP) phenomenon in variational quantum algorithms using a statistical approach. Using Gaussian function models, we identify three distinct types of BPs. The first type, which we called localized-dip BPs, occurs in landscapes that are mostly flat but contain a dip point where the gradient is large in a small region around the minimum. The second type, called localized-gorge BPs, which are somewhat similar to the localized-dip BPs but contain a gorge line. The third type, called everywhere-flat BPs, appears when the entire landscape is uniformly flat with almost vanishing gradients, making optimization significantly more difficult. After illustrating these behaviors in the Gaussian function models, we extend the analysis to the variational quantum eigensolver (VQE). We consider two types of ansätze: the hardware-efficient ansatz and the random Pauli ansatz. For both ansätze, we only observe the everywhere-flat BPs. Using our statistical approach, we searched for localized-dip and localized-gorge BPs but found no evidence of such features in the examples studied, suggesting that everywhere-flat BPs dominate in these ansätze. Our method effectively probes landscape features by capturing the gradient scaling across parameter space, making it a useful tool for diagnosing BPs in variational algorithms. To mitigate BPs in the VQE, we employ a genetic algorithm (GA) to optimize the random gates generated in the ansätze, thereby reshaping the cost function landscape to enhance the optimization efficiency. A comparison with an unoptimized ansatz shows how the ansatz design can improve the scalability and reliability of variational quantum algorithms.