Bee-yond the Plateau: Training QNNs with Swarm Algorithms

TL;DR

This study confirms BOA's potential to enhance the applicability of QNNs in complex quantum computations, and demonstrates the BOA's superior performance compared to the Adam algorithm.

Abstract

In the quest to harness the power of quantum computing, training quantum neural networks (QNNs) presents a formidable challenge. This study introduces an innovative approach, integrating the Bees Optimization Algorithm (BOA) to overcome one of the most significant hurdles -- barren plateaus. Our experiments across varying qubit counts and circuit depths demonstrate the BOA's superior performance compared to the Adam algorithm. Notably, BOA achieves faster convergence, higher accuracy, and greater computational efficiency. This study confirms BOA's potential in enhancing the applicability of QNNs in complex quantum computations.

Figures (1)

• Figure 1: Convergence of $|E(\Theta) - E_{gs}|$ versus iteration number. A Hamiltonian variational ansatz with periodic boundary conditions was employed to address the VQE task as described in \ref{['eq:VQE']}. The dashed horizontal line indicates the accuracy goal. Panel (a) displays the best performance of the Adam algorithm across 30 realizations, each starting from different random initial conditions. Panel (b) presents the results obtained using the BOA with a single swarm.