Resource Saving via Ensemble Techniques for Quantum Neural Networks

Explore the potential of ensemble techniques for quantum neural networks in this comprehensive lecture. Delve into the challenges faced by quantum neural networks due to limited qubits and hardware noise, and discover how ensemble methods can address these issues. Learn about the implementation of bagging and AdaBoost techniques with various data loading configurations, and examine their performance on both synthetic and real-world classification and regression tasks. Gain insights into experiments conducted on simulated, noiseless software and IBM superconducting-based QPUs, demonstrating how these techniques can mitigate quantum hardware noise. Understand the quantification of resources saved through these ensemble techniques, and discover how they enable the construction of large, powerful models on relatively small quantum devices. This 1 hour and 15 minute talk by Massimiliano Incudini from PCS Institute for Basic Science offers valuable knowledge for researchers and practitioners working at the intersection of quantum computing and machine learning.