Benchmarking Barren Plateau Mitigation Strategies in Quantum Neural Networks on Standard and Medical Image Datasets

Area of Science:

• Quantum Computing
• Machine Learning
• Artificial Intelligence

Background:

• Barren plateaus (BPs) are a significant challenge in training quantum neural networks (QNNs), leading to vanishing gradients as circuit complexity increases.
• Effective strategies are needed to ensure the trainability and scalability of QNNs for practical applications.

Purpose of the Study:

• To conduct a comparative benchmark of ten barren plateau mitigation strategies for quantum neural networks.
• To evaluate these strategies across various qubit settings and datasets of increasing complexity.

Main Methods:

• Benchmarking 10 barren plateau mitigation strategies, including initialization-based, model-based, and optimization-based methods.
• Experiments were performed using PennyLane and PyTorch on simulator backends with qubit settings from 2 to 20.
• Trainability was assessed using gradient variance and training loss on Iris, MNIST, and MedMNIST datasets.

Main Results:

• Under the benchmark conditions, tested mitigation strategies maintained measurable gradient variance and stable loss reduction, indicating that severe barren plateau behavior was not observed.
• CNN-based and Beta initialization demonstrated strong performance in retaining variance and achieving faster convergence.
• Gaussian initialization showed weaker performance in higher-dimensional settings.

Conclusions:

• The study provides a reproducible framework for evaluating barren plateau mitigation techniques in QNNs.
• Identified limitations include circuit depth, hardware noise, feature encoding, and classification performance, highlighting areas for future research in QNN benchmarking.