Quantum computing is expected to provide advantage over classical computing by running algorithms distinct from classical ones on quantum processors. With the circuit depth limited by the finite coherence time, it is difficult to deploy fully quantum algorithms to the near-term quantum processors. To circumvent this issue, quantum-classical hybrid algorithms based on the variational principle have been developed. Among them, Variational Quantum Eigensolvers (VQE) and Quantum Approximate Optimization Algorithm (QAOA) are promising candidates for simulating quantum systems and solving classical optimization problems, respectively. In these algorithms, an Adaptive Derivative Assembled Problem Tailored (ADAPT) approach leads to shallower quantum circuits than conventional methods. 

I have worked on properties and improvements of the adaptive variational algorithms for molecular simulation and classical optimization problems, as well as applying the adaptive strategy to non-variational algorithms for state preparation. My current interests include adaptive variational and non-variational quantum algorithms, Hamiltonian simulation, and impact of noise on the algorithms.

ADAPT-QAOA with a classically inspired initial state V. K. Sridhar, YC, B. Gard, E. Barnes, and S. E. Economou. arXiv:2310.09694

TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ansätze P. G. Anastasiou, YC, N. J. Mayhall, E. Barnes, and S. E. Economou. Phys. Rev. Research 6, 013254 (2024)

How Much Entanglement Do Quantum Optimization Algorithms Require? YC, L. Zhu, C. Liu, N. J. Mayhall, E. Barnes, and S. E. Economou. arXiv:2205.12283

Quantum algorithm for spectral projection by measuring an ancilla iteratively YC and T.-C. Wei. Phys. Rev. A 101, 032339 (2020)

Quantum processors are inherently noisy and it is vital to understand the physical origins of noise, characterize its strength, as well as reduce its impact on the computation result. Quantum error correction protects the logical information through redundancy. While fault tolerance through error correction is not yet fully achieved, another family of strategies seek to reduce the effect of noise through post-analysis and are termed quantum error mitigation. 

I have worked on quantum readout tomography and error mitigation, circuit performance benchmarking, and probabilistic error cancellation through a learning-based method. My current interests include circuit benchmarking, efficiency of error mitigation, and applying error mitigation to error corrected information.

Error statistics and scalability of quantum error mitigation formulas D, Qin, YC, and Y. Li. npj Quantum Information, 9, 35 (2023)

Learning-Based Quantum Error Mitigation A. Strikis, D. Qin, YC, S. C. Benjamin, and Y. Li. PRX Quantum 2, 040330 (2021)

Scalable Evaluation of Quantum-Circuit Error Loss Using Clifford Sampling Z. Wang, YC, Z. Song, D. Qin, H. Li, Q. Guo, H. Wang, C. Song, and Ying Li. Phys. Rev. Lett. 126, 080501 (2021)

Detector tomography on IBM quantum computers and mitigation of an imperfect measurement YC, M. Farahzad, S. Yoo, and T.-C. Wei. Phys. Rev. A 100, 052315 (2019)