Jin-Peng Liu

Peng, C., Liu, J.-P., Chern, G.-W., & Luo, D. (2024). Provably Efficient Adiabatic Learning for Quantum-Classical Dynamics. ArXiv. http://doi.org/10.48550/arXiv.2408.00276 (Original work published August 2024)

Liu, Z., Li, X., Wang, C., & Liu, J.-P. (2024). Toward end-to-end quantum simulation for protein dynamics. ArXiv. Retrieved from https://arxiv.org/abs/2411.03972 (Original work published November 2024)

An, D., Liu, J.-P., & Lin, L. (2023). Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost. Phys. Rev. Lett., 131. http://doi.org/10.1103/PhysRevLett.131.150603 (Original work published October 2023)

Childs, A. M., Li, T., Liu, J.-P., Wang, C., & Zhang, R. (2024). Quantum algorithms for sampling log-concave distributions and estimating normalizing constants. In Advances in Neural Information Processing Systems (NeurIPS 2022). Red Hook, NY, USA: Curran Associates Inc. http://doi.org/10.5555/3600270.3601956 (Original work published April 2024)

Childs, A. M., Liu, J.-P., & Ostrander, A. (2021). High-precision quantum algorithms for partial differential equations. Quantum 5, 574, 5. http://doi.org/10.22331/q-2021-11-10-574 (Original work published November 2021)

An, D., Linden, N., Liu, J.-P., Montanaro, A., Shao, C., & Wang, J. (2021). Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance. Quantum, 5, 481. http://doi.org/10.22331/q-2021-06-24-481 (Original work published June 2021)

Liu, J.-P., Kolden, H., Krovi, H., Loureiro, N., Trivisa, K., & Childs, A. M. (2021). Efficient quantum algorithm for dissipative nonlinear differential equations. Proceedings of the National Academy of Sciences, 118. http://doi.org/10.1073/pnas.2026805118 (Original work published March 2021)

Shao, C., & Liu, J.-P. (2020). Quantum algorithms for the polynomial eigenvalue problems. ArXiv. Retrieved from https://arxiv.org/abs/2010.15027 (Original work published October 2020)

Childs, A. M., & Liu, J.-P. (2020). Quantum spectral methods for differential equations. Commun. Math. Phys., 375, 1427–1457. http://doi.org/10.1007/s00220-020-03699-z (Original work published February 2020)

Sun, C., & Liu, J.-P. (2019). New stepsizes for the gradient method. Optim Lett. http://doi.org/10.1007/s11590-019-01512-y (Original work published January 2019)