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Practical Black Box Hamiltonian Learning


Jun 30, 2022
Andi Gu, Lukasz Cincio, Patrick J. Coles

* 32 pages, 10 figures 

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Inference-Based Quantum Sensing


Jun 20, 2022
C. Huerta Alderete, Max Hunter Gordon, Frederic Sauvage, Akira Sone, Andrew T. Sornborger, Patrick J. Coles, M. Cerezo

* 5+10 pages, 3+5 figures 

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Group-Invariant Quantum Machine Learning


May 04, 2022
Martin Larocca, Frederic Sauvage, Faris M. Sbahi, Guillaume Verdon, Patrick J. Coles, M. Cerezo

* 17 + 11 pages, 9 + 1 figures 

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Dynamical simulation via quantum machine learning with provable generalization


Apr 21, 2022
Joe Gibbs, Zoë Holmes, Matthias C. Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T. Sornborger, Patrick J. Coles

* Main text: 5 pages & 3 Figures. Supplementary Information: 12 pages & 2 Figures 

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Out-of-distribution generalization for learning quantum dynamics


Apr 21, 2022
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles, Zoë Holmes

* 7 pages (main body) + 14 pages (references and appendix); 4+1 figures 

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Covariance matrix preparation for quantum principal component analysis


Apr 07, 2022
Max Hunter Gordon, M. Cerezo, Lukasz Cincio, Patrick J. Coles

* 13 + 3 pages, 8 figures 

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The quantum low-rank approximation problem


Apr 01, 2022
Nic Ezzell, Zoë Holmes, Patrick J. Coles

* 9 pages, 1 figure 

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Generalization in quantum machine learning from few training data


Nov 09, 2021
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, Patrick J. Coles

* 14+25 pages, 4+1 figures 

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Subtleties in the trainability of quantum machine learning models


Oct 27, 2021
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem, Patrick J. Coles, M. Cerezo

* 12+12 pages, 8+2 figures 

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