Bee-yond the Plateau: Training QNNs with Swarm Algorithms

2024/08/16 by Rubén Darío Guerrero, Guerrero, Rubén Darío · 1 citation
Computer Science · #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2408.08836

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.

Cited by

• Variational-Adiabatic Quantum Solver for Systems of Linear Equations with Warm Starts

Related

• Sparks of Quantum Advantage and Rapid Retraining in Machine Learning
• Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
• Enhancing Variational Quantum Circuit Training: An Improved Neural Network Approach for Barren Plateau Mitigation
• Insights on Training Neural Networks for QUBO Tasks
• Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing