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)