Resource Saving via Ensemble Techniques for Quantum Neural Networks
Quantum neural networks hold significant promise for numerous applications,
particularly as they can be executed on the current generation of quantum
hardware. However, due to limited qubits or hardware noise, conducting
large-scale experiments often requires significant resources. Moreover, the
output of the model is susceptible to corruption by quantum hardware noise. To
address this issue, we propose the use of ensemble techniques, which involve
constructing a single machine learning model based on multiple instances of
quantum neural networks. In particular, we implement bagging and AdaBoost
techniques, with different data loading configurations, and evaluate their
performance on both synthetic and real-world classification and regression
tasks. To assess the potential performance improvement under different
environments, we conduct experiments on both simulated, noiseless software and
IBM superconducting-based QPUs, suggesting these techniques can mitigate the
quantum hardware noise. Additionally, we quantify the amount of resources saved
using these ensemble techniques. Our findings indicate that these methods
enable the construction of large, powerful models even on relatively small
quantum devices.
Massimiliano Incudini Michele Grossi Andrea Ceschini Antonio Mandarino Massimo Panella Sofia Vallecorsa David Windridge