Pulse-efficient Quantum Machine Learning

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André Melo^1,2, Nathan Earnest-Noble³, and Francesco Tacchino⁴

¹Kavli Institute of Nanoscience, Delft University of Technology, P.O. Box 4056, 2600 GA Delft, The Netherlands
²IBM Quantum, IBM Netherlands, Amsterdam, NH 1066 VH, The Netherlands
³IBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA
⁴IBM Quantum, IBM Research Europe – Zurich, 8803 Rüschlikon, Switzerland

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Abstract

Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level. A recent addition to this toolbox of techniques is pulse-efficient transpilation, which reduces circuit schedule duration by exploiting hardware-native cross-resonance interaction. In this work, we investigate the impact of pulse-efficient circuits on near-term algorithms for quantum machine learning. We report results for two standard experiments: binary classification on a synthetic dataset with quantum neural networks and handwritten digit recognition with quantum kernel estimation. In both cases, we find that pulse-efficient transpilation vastly reduces average circuit durations and, as a result, significantly improves classification accuracy. We conclude by applying pulse-efficient transpilation to the Hamiltonian Variational Ansatz and show that it delays the onset of noise-induced barren plateaus.

Popular summary

Quantum machine learning (QML) represents one of the most promising areas of application for near-term quantum computers. However, the scale and performances of many QML workflows are currently limited by the noise and imperfect operations present in today’s quantum processors. In this work, we use a newly devised technique, called pulse-efficient transpilation, to reduce the execution time of two popular quantum machine learning algorithms by leveraging hardware-native cross-resonance interactions. We show that this significantly improves the prediction accuracy of quantum neural networks and quantum kernel methods on both synthetic and real-world datasets, whilst requiring no additional overhead or calibrations on the user side. Our error suppression protocols can also be used to limit the impact of noise-induced barren plateaus, a well known threat to the trainability of QML models.

► BibTeX data

@article{Melo2023pulseefficient, doi = {10.22331/q-2023-10-09-1130}, url = {https://doi.org/10.22331/q-2023-10-09-1130}, title = {Pulse-efficient quantum machine learning}, author = {Melo, Andr{‘{e}} and Earnest-Noble, Nathan and Tacchino, Francesco}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{“{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {7}, pages = {1130}, month = oct, year = {2023}<br /> }

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Cited by

[1] Zhiding Liang, Zhixin Song, Jinglei Cheng, Zichang He, Ji Liu, Hanrui Wang, Ruiyang Qin, Yiru Wang, Song Han, Xuehai Qian, and Yiyu Shi, “Hybrid Gate-Pulse Model for Variational Quantum Algorithms”, arXiv:2212.00661, (2022).

[2] Zhiding Liang, Jinglei Cheng, Zhixin Song, Hang Ren, Rui Yang, Hanrui Wang, Kecheng Liu, Peter Kogge, Tongyang Li, Yongshan Ding, and Yiyu Shi, “Towards Advantages of Parameterized Quantum Pulses”, arXiv:2304.09253, (2023).

[3] Daniel J. Egger, Chiara Capecci, Bibek Pokharel, Panagiotis Kl. Barkoutsos, Laurin E. Fischer, Leonardo Guidoni, and Ivano Tavernelli, “Pulse variational quantum eigensolver on cross-resonance-based hardware”, Physical Review Research 5 3, 033159 (2023).

The above citations are from SAO/NASA ADS (last updated successfully 2023-10-10 03:11:59). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2023-10-10 03:11:58).

This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.

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Source: https://quantum-journal.org/papers/q-2023-10-09-1130/

Time Stamp: October 9, 2023

Time Stamp: Sep 8, 2022

Pulse-efficient quantum machine learning

Republished By Plato

Abstract

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