Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms

Abstract

Recent research has demonstrated that parametric quantum circuits (PQCs) are affected by gradients that progressively vanish to zero as a function of the number of qubits. We show that using a combination of gradient-free natural evolutionary strategy and gradient descent can mitigate the possibility of optimizing barren plateaus in the landscape. We implemented 2 specific methods: natural evolutionary strategy stochastic gradient descent (NESSGD) and natural evolutionary strategy adapting the step size according to belief in observed gradients (NESAdaBelief) to optimize PQC parameter values. They were compared with standard stochastic gradient descent, adaptive moment estimation, and a version of adaptive moment estimation adapting the step size according to belief in observed gradients in 5 classification tasks. NESSGD and NESAdaBelief demonstrated some superiority in 4 of the tasks. NESAdaBelief showed higher accuracy than AdaBelief in all 5 tasks. In addition, we investigated the applicability of NESSGD under the parameter shift rule and demonstrated that NESSGD can adapt to this rule, which means that our proposed method could also optimize the parameters of PQCs on quantum computers.