Resource saving via ensemble techniques for quantum neural networks

Abstract

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 conducted 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.

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

“…[ 2–4 ] Indeed, ML has been widely and successfully employed across experimental particle physics in recent years. [ 5–15 ] At the same time, the emergence of programmable quantum computers has led to intense interest in the newborn field of quantum machine learning [ 16–29 ] (QML), which under some circumstances may offer the potential for quantum advantage in ML tasks even on noisy intermediate‐scale quantum (NISQ) computers. [ 30 ] Following the successful application of classical ML algorithms, the potential for QML to yield new benefits in high energy physics has already begun to be explored, with promising, if preliminary, results reported to date.…”