En-Jui Kuo

Hsu, M.-C., Kuo, E.-J., Yu, W.-H., Cai, J.-F., & Hsieh, M.-H. (2022). Quantum state tomography via non-convex Riemannian gradient descent. ArXiv. Retrieved from https://arxiv.org/abs/2210.04717 (Original work published October 2022)

Hung, S.-H., & Kuo, E.-J. (2023). The Computational Complexity of Quantum Determinants. ArXiv. http://doi.org/10.48550/arxiv.2302.08083 (Original work published February 2023)

Xu, Y., Wang, Y., Kuo, E.-J., & Albert, V. (2023). Qubit-Oscillator Concatenated Codes: Decoding Formalism and Code Comparison. PRX Quantum, 4. http://doi.org/10.1103/prxquantum.4.020342 (Original work published June 2023)

Kuo, E.-J., Ware, B., Lunts, P., Hafezi, M., & White, C. D. (2024). Energy diffusion in weakly interacting chains with fermionic dissipation-assisted operator evolution. Phys. Rev. B. http://doi.org/10.1103/PhysRevB.110.075149 (Original work published August 2024)

Tsai, S.-T., Fields, E., Xu, Y., Kuo, E.-J., & Tiwary, P. (2022). Path sampling of recurrent neural networks by incorporating known physics. Nature Communications, 13(1). http://doi.org/10.1038/s41467-022-34780-x (Original work published November 2022)

Kuo, E.-J., Xu, Y., Hangleiter, D., Grankin, A., & Hafezi, M. (2022). Boson Sampling for Generalized Bosons. Physical Review Research, 4(4). http://doi.org/10.1103/physrevresearch.4.043096 (Original work published November 2022)

Kuo, E.-J., Seif, A., Lundgren, R., Whitsitt, S., & Hafezi, M. (2022). Decoding conformal field theories: from supervised to unsupervised learning. Physical Review Research, 4(043031). http://doi.org/10.1103/PhysRevResearch.4.043031 (Original work published October 2022)

Kuo, E.-J., & Dehghani, H. (2021). Unsupervised Learning of Symmetry Protected Topological Phase Transitions. ArXiv Preprint arXiv:2111.08747. Retrieved from https://arxiv.org/abs/2111.08747

Hong, C.-L., Tsai, T., Chou, J.-P., Chen, P.-J., Tsai, P.-K., Chen, Y.-C., … Goan, H.-S. (2022). Accurate and Efficient Quantum Computations of Molecular Properties Using Daubechies Wavelet Molecular Orbitals: A Benchmark Study against Experimental Data. PRX Quantum, 3(2). http://doi.org/10.1103/prxquantum.3.020360 (Original work published June 2022)

Kuo, E.-J., & Dehghani, H. (2022). Unsupervised learning of interacting topological and symmetry-breaking phase transitions. Physical Review B, 105(23). http://doi.org/10.1103/physrevb.105.235136 (Original work published June 2022)