John Preskill

Faist, P. ., Woods, M. ., Albert, V. ., Renes, J. ., Eisert, J. ., & Preskill, J. . (2023). Time-Energy Uncertainty Relation for Noisy Quantum Metrology. PRX Quantum, 4. http://doi.org/10.1103/prxquantum.4.040336 (Original work published December 2023)

Bauer, C. ., Davoudi, Z. ., Balantekin, B. ., Bhattacharya, T. ., Carena, M. ., de Jong, W. ., … Zorzetti, S. . (2023). Quantum Simulation for High-Energy Physics. PRX Quantum, 4. http://doi.org/10.1103/prxquantum.4.027001 (Original work published May 2023)

Huang, H.-Y. ., Kueng, R. ., Torlai, G. ., Albert, V. ., & Preskill, J. . (2022). Provably efficient machine learning for quantum many-body problems. Science, 377. http://doi.org/10.1126/science.abk3333 (Original work published September 2022)

Tong, Y. ., Albert, V. ., McClean, J. ., Preskill, J. ., & Su, Y. . (2022). Provably accurate simulation of gauge theories and bosonic systems. Quantum, 6, 816. http://doi.org/10.22331/q-2022-09-22-816 (Original work published September 2022)