Engineered Learning Problems on Noisy Quantum Processors
Abstract
Despite quantum machine learning being an active topic of research, demonstrations of algorithms run on quantum hardware beating classical methods remain rare. In this work, we demonstrate a significant performance gap using a projected quantum kernel for n=15,30 and $45$ qubits over classical methods on an engineered classification problem, and show reproducibility of the results over different quantum processor units. We also demonstrate how error mitigation can widen this gap, both by improving hardware performance but also by increasing the classical difficulty, with different techniques contributing unequally to these phenomena. This work provides strong evidence that superior empirical performance of quantum hardware over all classical techniques is an achievable goal in quantum machine learning, albeit for an artificial problem. However, as with any application relying on observables, beating state-of-the-art simulation methods remains the primary challenge, as our matrix product state results here show.
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BibTeX
@misc{payares2026engineered,
title = {{Engineered Learning Problems on Noisy Quantum Processors}},
author = {Esteban Payares and Nadia Milazzo and Ghada Ben Slama and Thomas Cope},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41848},
eprint = {2610.41848},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41848},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Engineered Learning Problems on Noisy Quantum Processors
AU - Esteban Payares
AU - Nadia Milazzo
AU - Ghada Ben Slama
AU - Thomas Cope
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41848
UR - https://arxiv.org/abs/2610.41848
AB - Despite quantum machine learning being an active topic of research, demonstrations of algorithms run on quantum hardware beating classical methods remain rare. In this work, we demonstrate a significant performance gap using a projected quantum kernel for n=15,30 and $45$ qubits over classical methods on an engineered classification problem, and show reproducibility of the results over different quantum processor units. We also demonstrate how error mitigation can widen this gap, both by improving hardware performance but also by increasing the classical difficulty, with different techniques contributing unequally to these phenomena. This work provides strong evidence that superior empirical performance of quantum hardware over all classical techniques is an achievable goal in quantum machine learning, albeit for an artificial problem. However, as with any application relying on observables, beating state-of-the-art simulation methods remains the primary challenge, as our matrix product state results here show.
ER -