| Gate set tomography E Nielsen, JK Gamble, K Rudinger, T Scholten, K Young, R Blume-Kohout Quantum 5, 557, 2021 | 245 | 2021 |
| Application-motivated, holistic benchmarking of a full quantum computing stack D Mills, S Sivarajah, TL Scholten, R Duncan Quantum 5, 415, 2021 | 56 | 2021 |
| Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor AH Karamlou, WA Simon, A Katabarwa, TL Scholten, B Peropadre, Y Cao npj Quantum Information 7, 2021 | 50 | 2021 |
| Behavior of the maximum likelihood in quantum state tomography TL Scholten, R Blume-Kohout New Journal of Physics 20 (2), 023050, 2018 | 33 | 2018 |
| Assessing the benefits and risks of quantum computers TL Scholten, CJ Williams, D Moody, M Mosca, W Hurley, WJ Zeng, ... arXiv preprint arXiv:2401.16317, 2024 | 16 | 2024 |
| Circuit knitting toolbox L Bello, AM Branczyk, S Bravyi, AC Vazquez, A Eddins, DJ Egger, B Fuller, ... | 11 | 2023 |
| Classifying single-qubit noise using machine learning TL Scholten, YK Liu, K Young, R Blume-Kohout arXiv preprint arXiv:1908.11762, 2019 | 8 | 2019 |
| Turbocharging quantum tomography RJ Blume-Kohout, JK Gamble, E Nielsen, PLW Maunz, TL Scholten, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2015 | 7 | 2015 |
| Qiskit: An open-source framework for quantum computing J Gambetta, DM Rodríguez, SP González, M Treinish, A Javadi-Abhari, ... DOI, 2021 | 5 | 2021 |
| A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes TL Scholten, D Perry II, J Washington, JR Glick, T Ward arXiv preprint arXiv:2307.04980, 2023 | 1 | 2023 |
| Kernel Matrix Completion for Offline Quantum-Enhanced Machine Learning A Naveh, I Fitzgerald, A Phan, A Lockwood, TL Scholten arXiv preprint arXiv:2112.08449, 2021 | 1 | 2021 |
| Machine-learned QCVV for distinguishing single-qubit noise T Scholten, YK Liu, K Young, R Blume-Kohout APS March Meeting Abstracts 2019, E27. 011, 2019 | | 2019 |
| Towards Scalable Characterization of Noisy, Intermediate-Scale Quantum Information Processors TL Scholten University of New Mexico, 2018 | | 2018 |
| High-Accuracy Classification of Single-Qubit Noise via Machine Learning. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2018 | | 2018 |
| A Few Thoughts on Characterizing Quantum Hardware. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2018 | | 2018 |
| On the edge: Geometry model selection and quantum compressed sensing. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2018 | | 2018 |
| Machine Learning of Noise in Single-Qubit Hardware T Scholten, R Blume-Kohout APS March Meeting Abstracts 2018, C39. 007, 2018 | | 2018 |
| Learning Noise in Quantum Information Processors. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017 | | 2017 |
| An Effective State Space Dimension For A Quantum System. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2016 | | 2016 |
| Tomographing Quantum State Tomography. TL Scholten Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2016 | | 2016 |
Robin Blume-KohoutDistinguished Member of Technical Staff, Sandia National LabsVerified email at sandia.gov