Papers › Large-scale quantum reservoir learning with an analog quantum computer
Large-scale quantum reservoir learning with an analog quantum computer
Milan Kornjača, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Sergio H. Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, Kevin Bagnall, James I. Basham, Joseph Campo, Adam Choukri, Robert DeAngelo, Paige Frederick, David Haines, Julian Hammett, Ning Hsu, Ming-Guang Hu, Florian Huber, Paul Niklas Jepsen, Ningyuan Jia, Thomas Karolyshyn, Minho Kwon, John Long, Jonathan Lopatin, Alexander Lukin, Tommaso Macrì, Ognjen Marković, Luis A. Martínez-Martínez, Xianmei Meng, Evgeny Ostroumov, David Paquette, John Robinson, Pedro Sales Rodriguez, Anshuman Singh, Nandan Sinha, Henry Thoreen, Noel Wan, Daniel Waxman-Lenz, Tak Wong, Kai-Hsin Wu, Pedro L. S. Lopes, Yuval Boger, Nathan Gemelke, Takuya Kitagawa, Alexander Keesling, Xun Gao, Alexei Bylinskii, Susanne F. Yelin, Fangli Liu, Sheng-Tao Wang
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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.
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