[23] Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley, Kevin J. Satzinger, Frank Arute, Kunal Arya, Juan Atalaya, Joseph C. Bardin, Rami Barends, Sergio Boixo, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett, Nicholas Bushnell, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Sean Demura, Andrew Dunsworth, Daniel Eppens, Austin Fowler, Brooks Foxen, Craig Gidney, Marissa Giustina, Rob Graff, Steve Habegger, Alan Ho, Sabrina Hong, Trent Huang, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody Jones, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Seon Kim, Paul V. Klimov, Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Pavel Laptev, Mike Lindmark, Martin Leib, Orion Martin, John M. Martinis, Jarrod R. McClean, Matt McEwen, Anthony Megrant, Xiao Mi, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Charles Neill, Florian Neukart, Murphy Yuezhen Niu, Thomas E. O'Brien, Bryan O'Gorman, Eric Ostby, Andre Petukhov, Harald Putterman, Chris Quintana, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Andrea Skolik, Vadim Smelyanskiy, Doug Strain, Michael Streif, Marco Szalay, Amit Vainsencher, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Leo Zhou, Hartmut Neven, Dave Bacon, Erik Lucero, Edward Farhi, and Ryan Babbush. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nature Physics, 17(3):332-336, Mar 2021. ISSN 1745-2481. doi: 10.1038/s41567-020-01105-y. URL https://doi.org/10.1038/s41567-020-01105-y.
[24] Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta. Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747):209–212, mar 2019. doi: 10.1038/s41586-019-0980-2. URL https://doi.org/10.1038%2Fs41586-019-0980-2.
[25] Mohsen Heidari, Ananth Grama, and Wojciech Szpankowski. Toward physically realizable quantum neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 6902-6909, 2022.
[26] Maziar S Hemati, Matthew O Williams, and Clarence W Rowley. Dynamic mode decomposition for large and streaming datasets. Physics of Fluids, 26(11), 2014.
[27] Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, and Jarrod R. McClean. Quantum advantage in learning from experiments. Science, 376(6598):1182-1186, jun 2022. doi: 10.1126/science.abn7293. URL https://doi.org/10.1126/2Fscience.abn7293.
[28] Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V. Albert, and John Preskill. Provably efficient machine learning for quantum many-body problems. Science, 377(6613), sep 2022. doi: 10.1126/science.abk3333. URL https://doi.org/10.1126/2Fscience.abk3333.
[29] Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, and Vedran Dunjko. Quantum machine learning beyond kernel methods, 2021. URL https://arxiv.org/abs/2110.13162.
[30] Mason Kamb, Eurika Kaiser, Steven L Brunton, and J Nathan Kutz. Time-delay observables for koopman: Theory and applications. SIAM Journal on Applied Dynamical Systems, 19(2): 886-917, 2020.
[31] Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. nature, 549(7671):242-246, 2017.
[32] Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, and Anupam Prakash. q-means: A quantum algorithm for unsupervised machine learning. Advances in Neural Information Processing Systems, 32, 2019.
[33] Sami Khairy, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash. Learning to optimize variational quantum circuits to solve combinatorial problems. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 2367-2375, 2020.
[34] N. Klco, E. F. Dumitrescu, A. J. McCaskey, T. D. Morris, R. C. Pooser, M. Sanz, E. Solano, P. Lougovski, and M. J. Savage. Quantum-classical computation of schwinger model dynamics using quantum computers. Phys. Rev. A, 98:032331, Sep 2018. doi: 10.1103/PhysRevA.98.032331. URL https://link.aps.org/doi/10.1103/PhysRevA.98.032331.