Towards understanding the power of quantum kernels in the NISQ era
| Autor: | Dacheng Tao, Yong Luo, Xinbiao Wang, Yuxuan Du |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Quantum Physics Computer Science - Machine Learning Theoretical computer science Physics and Astronomy (miscellaneous) Quantum machine learning Field (physics) Computer science Physics QC1-999 FOS: Physical sciences Atomic and Molecular Physics and Optics Machine Learning (cs.LG) Kernel method ComputerSystemsOrganization_MISCELLANEOUS Quantum system Noise (video) Quantum Physics (quant-ph) Quantum Kernel (category theory) Quantum computer |
| Zdroj: | Quantum, Vol 5, p 531 (2021) |
| Popis: | A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can achieve quantum advantages. Recently, Huang et al. [Nat Commun 12, 2631] partially answered this question by the lens of quantum kernel learning. Namely, they exhibited that quantum kernels can learn specific datasets with lower generalization error over the optimal classical kernel methods. However, most of their results are established on the ideal setting and ignore the caveats of near-term quantum machines. To this end, a crucial open question is: does the power of quantum kernels still hold under the NISQ setting? In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered. Concretely, we first prove that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise. With the aim of preserving the superiority of quantum kernels in the NISQ era, we further devise an effective method via indefinite kernel learning. Numerical simulations accord with our theoretical results. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices. |
| Databáze: | OpenAIRE |
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