Bee-yond the plateau: Training QNNs with swarm algorithms

Area of Science:

• Quantum Computing
• Artificial Intelligence
• Optimization Algorithms

Background:

• Training quantum neural networks (QNNs) is crucial for advancing quantum computing.
• Barren plateaus pose a significant challenge, hindering effective QNN training.
• Existing optimization algorithms like Adam may struggle with QNN training complexities.

Purpose of the Study:

• To introduce and evaluate the Bees Optimization Algorithm (BOA) for training QNNs.
• To address the barren plateau problem in quantum machine learning.
• To compare BOA's performance against established optimization methods.

Main Methods:

• Integration of the Bees Optimization Algorithm (BOA) into the QNN training process.
• Experimental evaluation across various qubit counts and circuit depths.
• Comparative analysis with the Adam optimization algorithm.

Main Results:

• BOA demonstrated superior performance in training QNNs compared to the Adam algorithm.
• BOA achieved faster convergence rates.
• BOA resulted in higher accuracy and improved computational efficiency.

Conclusions:

• The Bees Optimization Algorithm (BOA) is a promising solution for overcoming barren plateaus in QNNs.
• BOA enhances the practical applicability of QNNs for complex quantum computations.
• This study validates BOA's effectiveness in quantum machine learning.