Pulse-efficient quantum machine learning
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 exponent...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-10-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-10-09-1130/pdf/ |
| _version_ | 1797663209513025536 |
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| author | André Melo Nathan Earnest-Noble Francesco Tacchino |
| author_facet | André Melo Nathan Earnest-Noble Francesco Tacchino |
| author_sort | André Melo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T19:11:14Z |
| format | Article |
| id | doaj.art-82fbb555ac6e4b1180e1ca9ed0148884 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-03-11T19:11:14Z |
| publishDate | 2023-10-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-82fbb555ac6e4b1180e1ca9ed01488842023-10-09T15:11:57ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2023-10-017113010.22331/q-2023-10-09-113010.22331/q-2023-10-09-1130Pulse-efficient quantum machine learningAndré MeloNathan Earnest-NobleFrancesco TacchinoQuantum 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.https://quantum-journal.org/papers/q-2023-10-09-1130/pdf/ |
| spellingShingle | André Melo Nathan Earnest-Noble Francesco Tacchino Pulse-efficient quantum machine learning Quantum |
| title | Pulse-efficient quantum machine learning |
| title_full | Pulse-efficient quantum machine learning |
| title_fullStr | Pulse-efficient quantum machine learning |
| title_full_unstemmed | Pulse-efficient quantum machine learning |
| title_short | Pulse-efficient quantum machine learning |
| title_sort | pulse efficient quantum machine learning |
| url | https://quantum-journal.org/papers/q-2023-10-09-1130/pdf/ |
| work_keys_str_mv | AT andremelo pulseefficientquantummachinelearning AT nathanearnestnoble pulseefficientquantummachinelearning AT francescotacchino pulseefficientquantummachinelearning |