My research is dedicated to advancing the design and analysis of quantum algorithms, with a focus on comprehensively understanding the capabilities and limitations of both near-term and fault-tolerant quantum computers. I actively contribute to the development of quantum algorithms for a myriad of applications, including solving partial differential equations and addressing combinatorial optimisation problems. Moreover, I am involved in designing more efficient and noise-resistant methods for learning the properties of quantum systems. Notably, one such method is classical shadow tomography, which enables the estimation of quantum system properties using limited resources. Through these endeavours, my objective is to propel quantum algorithm techniques forward, with tangible impacts ranging from enhancing computational efficiency to enriching our comprehension of quantum systems.
Webpage: https://smt.sutd.edu.sg/people/faculty/koh-enshan-dax
Selected Publications:
- An expressive ansatz for low-depth quantum approximate optimisation, Quantum Science and Technology 9, 025010 (2024)
- Classical shadows with Pauli-invariant unitary ensembles, npj Quantum Information 10, 6 (2024)
- Classical Shadows with Noise, Quantum 6, 776 (2022)
- Minimizing Estimation Runtime on Noisy Quantum Computers, PRX Quantum 2, 010346 (2021)
- Efficient Classical Simulation of Clifford Circuits with Nonstabilizer Input States, Physical Review Letters 123, 170502 (2019)
