Bee-yond the Plateau: Training QNNs with Swarm Algorithms

TL;DR

This paper presents a novel method using swarm algorithms, specifically the Bees Optimization Algorithm, to effectively train quantum neural networks and overcome barren plateau challenges, outperforming traditional optimizers like Adam.

Contribution

The study introduces the application of BOA for QNN training, demonstrating improved convergence speed, accuracy, and efficiency over existing methods.

Findings

BOA outperforms Adam in training QNNs.

BOA achieves faster convergence and higher accuracy.

BOA demonstrates greater computational efficiency.

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.