Publications

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J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., Ray-based classification framework for high-dimensional data, Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada, 2020.
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X. Xu, Kim, S., Bahl, G., and Taylor, J. M., A quasi-mode theory of chiral phonons, 2016.
V. Dunjko, Taylor, J. M., and Briegel, H. J., Quantum-Enhanced Machine Learning, Physical Review Letters, vol. 117, no. 13, p. 130501, 2016.
Y. Wang, Tran, M. C., and Taylor, J. M., Quantum simulation of ferromagnetic Heisenberg model, 2017.
X. Xu, Gullans, M., and Taylor, J. M., Quantum Nonlinear Optics Near Optomechanical Instabilities, Physical Review A, vol. 91, no. 1, p. 013818, 2015.
M. Gullans and Taylor, J. M., A Quantum Network of Silicon Qubits using Mid-Infrared Graphene Plasmons, 2014.
C. - H. Wang and Taylor, J. M., A Quantum Model for an Entropic Spring, Physical Review B, vol. 93, no. 21, p. 214102, 2016.
D. Kielpinski, Kafri, D., Woolley, M. J., Milburn, G. J., and Taylor, J. M., Quantum interface between an electrical circuit and a single atom, Physical Review Letters, vol. 108, no. 13, 2012.
J. M. Taylor, Giedke, G., Christ, H., Paredes, B., Cirac, J. I., Zoller, P., Lukin, M. D., and Imamoglu, A., Quantum information processing using localized ensembles of nuclear spins, 2004.
J. M. Taylor, A quantum dot implementation of the quantum NAND algorithm, 2007.
P. S. Emani, Warrell, J., Anticevic, A., Bekiranov, S., Gandal, M., McConnell, M. J., Sapiro, G., Aspuru-Guzik, A., Baker, J., Bastiani, M., McClure, P., Murray, J., Sotiropoulos, S. N., Taylor, J. M., Senthil, G., Lehner, T., Gerstein, M. B., and Harrow, A. W., Quantum Computing at the Frontiers of Biological Sciences, 2019.
J. P. Zwolak, Kalantre, S. S., Wu, X., Ragole, S., and Taylor, J. M., QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments, PLOS ONE, vol. 13, no. 10, p. e0205844, 2018.