Large-scale quantum reservoir learning with an analog quantum computer
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arXiv
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| Format: | Preprint |
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2024
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| author | Kornjača, Milan Hu, Hong-Ye Zhao, Chen Wurtz, Jonathan Weinberg, Phillip Hamdan, Majd Zhdanov, Andrii Cantu, Sergio H. Zhou, Hengyun Bravo, Rodrigo Araiza Bagnall, Kevin Basham, James I. Campo, Joseph Choukri, Adam DeAngelo, Robert Frederick, Paige Haines, David Hammett, Julian Hsu, Ning Hu, Ming-Guang Huber, Florian Jepsen, Paul Niklas Jia, Ningyuan Karolyshyn, Thomas Kwon, Minho Long, John Lopatin, Jonathan Lukin, Alexander Macrì, Tommaso Marković, Ognjen Martínez-Martínez, Luis A. Meng, Xianmei Ostroumov, Evgeny Paquette, David Robinson, John Rodriguez, Pedro Sales Singh, Anshuman Sinha, Nandan Thoreen, Henry Wan, Noel Waxman-Lenz, Daniel Wong, Tak Wu, Kai-Hsin Lopes, Pedro L. S. Boger, Yuval Gemelke, Nathan Kitagawa, Takuya Keesling, Alexander Gao, Xun Bylinskii, Alexei Yelin, Susanne F. Liu, Fangli Wang, Sheng-Tao |
| author_facet | Kornjača, Milan Hu, Hong-Ye Zhao, Chen Wurtz, Jonathan Weinberg, Phillip Hamdan, Majd Zhdanov, Andrii Cantu, Sergio H. Zhou, Hengyun Bravo, Rodrigo Araiza Bagnall, Kevin Basham, James I. Campo, Joseph Choukri, Adam DeAngelo, Robert Frederick, Paige Haines, David Hammett, Julian Hsu, Ning Hu, Ming-Guang Huber, Florian Jepsen, Paul Niklas Jia, Ningyuan Karolyshyn, Thomas Kwon, Minho Long, John Lopatin, Jonathan Lukin, Alexander Macrì, Tommaso Marković, Ognjen Martínez-Martínez, Luis A. Meng, Xianmei Ostroumov, Evgeny Paquette, David Robinson, John Rodriguez, Pedro Sales Singh, Anshuman Sinha, Nandan Thoreen, Henry Wan, Noel Waxman-Lenz, Daniel Wong, Tak Wu, Kai-Hsin Lopes, Pedro L. S. Boger, Yuval Gemelke, Nathan Kitagawa, Takuya Keesling, Alexander Gao, Xun Bylinskii, Alexei Yelin, Susanne F. Liu, Fangli Wang, Sheng-Tao |
| contents | Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large-scale quantum reservoir learning with an analog quantum computer Kornjača, Milan Hu, Hong-Ye Zhao, Chen Wurtz, Jonathan Weinberg, Phillip Hamdan, Majd Zhdanov, Andrii Cantu, Sergio H. Zhou, Hengyun Bravo, Rodrigo Araiza Bagnall, Kevin Basham, James I. Campo, Joseph Choukri, Adam DeAngelo, Robert Frederick, Paige Haines, David Hammett, Julian Hsu, Ning Hu, Ming-Guang Huber, Florian Jepsen, Paul Niklas Jia, Ningyuan Karolyshyn, Thomas Kwon, Minho Long, John Lopatin, Jonathan Lukin, Alexander Macrì, Tommaso Marković, Ognjen Martínez-Martínez, Luis A. Meng, Xianmei Ostroumov, Evgeny Paquette, David Robinson, John Rodriguez, Pedro Sales Singh, Anshuman Sinha, Nandan Thoreen, Henry Wan, Noel Waxman-Lenz, Daniel Wong, Tak Wu, Kai-Hsin Lopes, Pedro L. S. Boger, Yuval Gemelke, Nathan Kitagawa, Takuya Keesling, Alexander Gao, Xun Bylinskii, Alexei Yelin, Susanne F. Liu, Fangli Wang, Sheng-Tao Quantum Physics Disordered Systems and Neural Networks Atomic Physics Quantum machine learning has gained considerable attention as quantum technology advances, presenting a promising approach for efficiently learning complex data patterns. Despite this promise, most contemporary quantum methods require significant resources for variational parameter optimization and face issues with vanishing gradients, leading to experiments that are either limited in scale or lack potential for quantum advantage. To address this, we develop a general-purpose, gradient-free, and scalable quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks, including binary and multi-class classification, as well as timeseries prediction. Effective and improving learning is observed with increasing system sizes of up to 108 qubits, demonstrating the largest quantum machine learning experiment to date. We further observe comparative quantum kernel advantage in learning tasks by constructing synthetic datasets based on the geometric differences between generated quantum and classical data kernels. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning. We expect these results to stimulate further extensions to different quantum hardware and machine learning paradigms, including early fault-tolerant hardware and generative machine learning tasks. |
| title | Large-scale quantum reservoir learning with an analog quantum computer |
| topic | Quantum Physics Disordered Systems and Neural Networks Atomic Physics |
| url | https://arxiv.org/abs/2407.02553 |