CV
Education
- Ph.D. Physics, University of California, Berkeley
Supervisor: K. Birgitta Whaley - B.Sc. Honours Mathematics and Physics, McGill University
Experience
- Senior Research Scientist, Q-CTRL
02/2024–Present Los Angeles, United States- Designed and implemented a novel protocol for long-range CNOT gates that significantly outperforms the alternative measurement-based protocol, achieving the highest-fidelity CNOT gate teleportation across up to at least 40 lattice sites on solid-state devices to date. Leveraged limited error detection schemes to enable 75-qubit GHZ state preparation. (PRX Quantum)
- Efforts toward hamiltonian simulation and quantum error correction.
- Research Intern, IBM Quantum
02/2023–08/2023 New York, United States- Pauli channel estimation using error-mitigated entanglement-enhanced benchmarking, exponentially reducing sampling complexity compared to cycle benchmarking. (arXiv)
- Developed a machine learning-based quantum error mitigation technique that significantly reduces the cost of traditional quantum error mitigation methods without sacrificing accuracy on classically intractable circuits. Showed its superiority for variational algorithms such as VQE, and tomography-like experiments. (Nat. Mach. Intell. 2024, NeuIPS AI4Science 2023)
- Crosstalk suppression by context-aware compilation absorbing transmon crosstalk into the Hamiltonian in many-body physics simulations. (Proceedings of ISCA 2024)
- Ph.D. Student Researcher, UC Berkeley
09/2018–12/2023 Berkeley, United States- Derived the robustness of general quantum classifiers against worst-case input perturbations based on concentration of measure phenomenon. Proved that the practical robustness of generic quantum classifiers decreases only mildly polynomially in the number of qubits, in contrast to the extreme vulnerability previously believed. (Phys. Rev. A 2021)
- Developed a protocol for continuous quantum error correction on dispersive readout signals on superconducting qubits. The protocol decodes and applies active corrections. Benchmarked against Bayesian inference model on tasks including quantum annealing. (New J. Phys. 2022)
- Analytically and numerically studied decoherence in tensor network discriminative QML models and their connections to classical Bayesian networks. Showed that adding noisy ancillas helps mitigate decoherence in simulations with distributed GPU training. (Quantum Mach. Intell. 2023)
- Collaborated on showing tensor networks with boundary-law entanglement entropy are suitable for classifying datasets with similar mutual information scaling. (Mach. Learn.: Sci. Technol. 2021)
Publications
Academic Service
- Reviewed for Journals [ORCID, WoS] :
- npj Quantum Information
- Quantum Science and Technology
- New Journal of Physics
- Journal of Physics A: Mathematical and Theoretical
- Machine Learning: Science and Technology
- Quantum Machine Intelligence
- npj Quantum Information
- IOP Trusted Reviewer status
In recognition of exceptionally high level of peer review competency
Talks & Seminars
- 03/25, APS Global Physics Summit
- 11/24, NSF Workshop on Real-Time Control
- 03/24, APS March Meeting
- 12/23, NeuIPS AI4Science Workshop
- 11/23, Unitary Fund Seminar
- 10/23, Qiskit Seminar
- 09/23, IEEE Quantum Week
- 03/22, APS March Meeting
- 01/22, Quantum System Accelerator (QSA) Seminar
- 10/21, Quantum Techniques in Machine Learning (QTML)
- 03/21, APS March Meeting
Patents
- 01/24, Suppression of correlated noise in quantum computers (IBM Quantum)
- 09/23, Performing quantum error mitigation at runtime using trained machine learning model (IBM Quantum)
Teaching
UC Berkeley:
- PHYS 288 Bayesian, and ML for Physics, 2023
- PHYS 112 Statistical Mechanics, 2019
- PHYS 5B Electromagnetism, Waves, and Optics, 2019
- PHYS 7B Physics for Scientists and Engineers, 2018
McGill: