John
Preskill
Tan, S., Pattison, C., McEwen, M., & Preskill, J. (2024). Resilience of the surface code to error bursts. ArXiv. Retrieved from https://arxiv.org/abs/2406.18897 (Original work published June 2024)
Albert, V., Aasen, D., Xu, W., Ji, W., Alicea, J., & Preskill, J. (2021). Spin chains, defects, and quantum wires for the quantum-double edge. ArXiv. Retrieved from https://arxiv.org/abs/2111.12096 (Original work published November 2021)
Faist, P., Nezami, S., Albert, V., Salton, G., Pastawski, F., Hayden, P., & Preskill, J. (2020). Continuous symmetries and approximate quantum error correction. Phys. Rev. X, 10. http://doi.org/10.1103/PhysRevX.10.041018 (Original work published October 2020)
Albert, V., Covey, J., & Preskill, J. (2020). Robust Encoding of a Qubit in a Molecule. Phys. Rev. X, 10. http://doi.org/10.1103/PhysRevX.10.031050 (Original work published September 2020)
Jordan, S., Krovi, H., Lee, K., & Preskill, J. (2018). BQP-completeness of Scattering in Scalar Quantum Field Theory. Quantum, 2, 44. http://doi.org/10.22331/q-2018-01-08-44 (Original work published January 2018)
Jordan, S., Lee, K., & Preskill, J. (2014). Quantum Computation of Scattering in Scalar Quantum Field Theories. Quantum Information and Computation, 14, 1014–1080. Retrieved from http://arxiv.org/abs/1112.4833v1 (Original work published September 2014)
Jordan, S., Lee, K., & Preskill, J. (2014). Quantum Algorithms for Fermionic Quantum Field Theories. ArXiv. Retrieved from http://arxiv.org/abs/1404.7115v1 (Original work published April 2014)
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)