Yi-Kai Liu | QuICS

Adjunct Associate Professor and Co-Director

yi-kai.liu@nist.gov

3100J Atlantic Building

(301) 314-1850

Yi-Kai Liu is Co-Director of QuICS, an Adjunct Associate Professor in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and a staff scientist in the Applied and Computational Mathematics Division at the National Institutes of Standards and Technology (NIST). Liu's research centers on quantum computation, in particular, quantum algorithms and complexity, quantum state tomography and cryptography. He also works on related topics in compressed sensing and machine learning. He received his doctorate in computer science from the University of California, San Diego in 2007.

Publications

2024

L. Pedro García-Pintos, O'Leary, T., Biswas, T., Bringewatt, J., Cincio, L., Brady, L. T., and Liu, Y. - K., “Resilience-Runtime Tradeoff Relations for Quantum Algorithms”, 2024.

J. Rajakumar, Watson, J. D., and Liu, Y. - K., “Polynomial-Time Classical Simulation of Noisy IQP Circuits with Constant Depth”, 2024.

Z. Liu, Devulapalli, D., Hangleiter, D., Liu, Y. - K., Kollár, A. J., Gorshkov, A. V., and Childs, A. M., “Efficiently verifiable quantum advantage on near-term analog quantum simulators”, 2024.

2023

K. Huang, Farfurnik, D., Seif, A., Hafezi, M., and Liu, Y. - K., “Random Pulse Sequences for Qubit Noise Spectroscopy”, 2023.

Y. - K. Liu, “An Uncertainty Principle for the Curvelet Transform, and the Infeasibility of Quantum Algorithms for Finding Short Lattice Vectors”, 2023.

Y. - K. Liu, “An Uncertainty Principle for the Curvelet Transform, and the Infeasibility of Quantum Algorithms for Finding Short Lattice Vectors”, 2023.

2021

A. Seif, Hafezi, M., and Liu, Y. - K., “Compressed Sensing Measurement of Long-Range Correlated Noise”, 2021.

2020

G. Alagic, Alperin-Sheriff, J., Apon, D., Cooper, D., Dang, Q., Kelsey, J., Liu, Y. - K., Miller, C., Moody, D., Peralta, R., Perlner, R., Robinson, A., and Smith-Tone, D., “Status Report on the Second Round of the NIST Post-Quantum Cryptography Standardization Process”, NISTIR 8309, 2020.

2019

T. L. Scholten, Liu, Y. - K., Young, K., and Blume-Kohout, R., “Classifying single-qubit noise using machine learning”, 2019.

G. Alagic, Alperin-Sheriff, J., Apon, D., Cooper, D., Dang, Q., Miller, C., Moody, D., Peralta, R., Perlner, R., Robinson, A., Smith-Tone, D., and Liu, Y. - K., “Status Report on the First Round of the NIST Post-Quantum Cryptography Standardization Process”, School: National Institute for Standards and Technology , 2019.

2018

S. P. Jordan and Liu, Y. - K., “Quantum Cryptanalysis: Shor, Grover, and Beyond”, IEEE Security & Privacy , vol. 16, no. 5, pp. 14-21, 2018.

P. Bierhorst, Knill, E., Glancy, S., Zhang, Y., Mink, A., Jordan, S., Rommal, A., Liu, Y. - K., Christensen, B., Nam, S. Woo, Stevens, M. J., and Shalm, L. K., “Experimentally Generated Randomness Certified by the Impossibility of Superluminal Signals”, Nature, vol. 556, pp. 223-226, 2018.

I. Roth, Kueng, R., Kimmel, S., Liu, Y. - K., Gross, D., Eisert, J., and Kliesch, M., “Recovering quantum gates from few average gate fidelities”, Phys. Rev. Lett. , vol. 121, p. 170502, 2018.

F. Krahmer and Liu, Y. - K., “Phase Retrieval Without Small-Ball Probability Assumptions”, IEEE Transactions on Information Theory , vol. 64, no. 1, pp. 485-500, 2018.

Z. Ji, Liu, Y. - K., and Song, F., “Pseudorandom States, Non-Cloning Theorems and Quantum Money”, In: Shacham H., Boldyreva A. (eds) Advances in Cryptology – CRYPTO 2018. CRYPTO 2018. Lecture Notes in Computer Science., vol. 10993, 2018.

2017

V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., “Super-polynomial and exponential improvements for quantum-enhanced reinforcement learning”, 2017.

R. Perlner and Liu, Y. - K., “Thermodynamic Analysis of Classical and Quantum Search Algorithms”, 2017.

S. Kimmel and Liu, Y. - K., “Phase retrieval using unitary 2-designs”, in SampTA 2017, 2017.

P. Bierhorst, Knill, E., Glancy, S., Mink, A., Jordan, S. P., Rommal, A., Liu, Y. - K., Christensen, B., Nam, S. Woo, and Shalm, L. K., “Experimentally Generated Random Numbers Certified by the Impossibility of Superluminal Signaling”, 2017.

V. Dunjko, Liu, Y. - K., Wu, X., and Taylor, J. M., “Exponential improvements for quantum-accessible reinforcement learning”, 2017.