Jacob
M.
Taylor
Zwolak, J. P., Taylor, J. M., Andrews, R. ., Benson, J. ., Bryant, G. ., Buterakos, D. ., … Weber, B. . (2024). Data needs and challenges for quantum dot devices automation. Npj Quantum Information, 10. http://doi.org/10.1038/s41534-024-00878-x (Original work published October 2024)
Premawardhana, G. ., Kunjummen, J. ., Subhankar, S. ., & Taylor, J. M. (2024). Feasibility of a trapped atom interferometer with accelerating optical traps. Phys. Rev. A, 109, 053316. http://doi.org/10.1103/PhysRevA.109.053316 (Original work published May 2024)
Barker, D. S., Carney, D. ., LeBrun, T. ., Moore, D. ., & Taylor, J. M. (2024). Collision-resolved pressure sensing. Phys. Rev. A, 109, 042616. http://doi.org/10.1103/PhysRevA.109.042616 (Original work published April 2024)
Richman, B. ., Ghosh, S. ., Carney, D. ., Higgins, G. ., Shawhan, P. ., Lobb, C. J., & Taylor, J. M. (2024). Backaction-evading receivers with magnetomechanical and electromechanical sensors. Phys. Rev. Res., 6, 023141. http://doi.org/10.1103/PhysRevResearch.6.023141 (Original work published May 2024)
Gandhari, S. ., Albert, V. ., Gerrits, T. ., Taylor, J. M., & Gullans, M. J. (2024). Precision Bounds on Continuous-Variable State Tomography using Classical Shadows. PRX Quantum, 5(1), 13. http://doi.org/10.1103/PRXQuantum.5.010346 (Original work published March 2024)
Zwolak, J. P., McJunkin, T. ., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., & Taylor, J. M. (2021). Ray-Based Framework for State Identification in Quantum Dot Devices. Prx Quantum, 2. http://doi.org/10.1103/PRXQuantum.2.020335 (Original work published June 2021)
Ziegler, J. ., McJunkin, T. ., Joseph, E. S., Kalantre, S. S., Harpt, B. ., Savage, D. E., … Zwolak, J. P. (2022). Toward Robust Autotuning of Noisy Quantum dot Devices. Physical Review Applied, 17. http://doi.org/10.1103/PhysRevApplied.17.024069 (Original work published February 2022)
Zwolak, J. P., Kalantre, S. S., . Y. Wu, X. ., Ragole, S. ., & Taylor, J. M. (2018). QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. Plos One, 13. http://doi.org/10.1371/journal.pone.0205844 (Original work published October 2018)
Zwolak, J. P., McJunkin, T. ., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., … Taylor, J. M. (2020). Autotuning of Double-Dot Devices In Situ with Machine Learning. Physical Review Applied, 13. http://doi.org/10.1103/PhysRevApplied.13.034075 (Original work published April 2020)
Zajac, D. M., Sigillito, A. J., Russ, M. ., Borjans, F. ., Taylor, J. M., Burkard, G. ., & Petta, J. R. (2018). Resonantly driven CNOT gate for electron spins. Science, 359, 439–442. http://doi.org/10.1126/science.aao5965 (Original work published January 2018)