Zhiding Liang 梁之鼎
CUHK
Zhiding Liang is an assistant professor at the CUHK CSE. He was an assistant professor at Rensselaer Polytechnic Institute (RPI) CS department from 2024 - 2025. He received PhD degree from the Department of Computer Science and Engineering in 2024. The results of his research have been published in prestigious conferences and journals, including ISCA, HPCA, DAC, SIGMETRICS, ICCAD, Neurips, QCE, TCAD, TSG, TQE, and TVCG. He has been selected as a DAC Young Fellow in both 2021 and 2022, the ML and Systems Rising Star in 2025. He has also been nominated as the recipient of the Edison Innovation Fellowship by the IDEA Center at the University of Notre Dame. He is devoted to quantum education and outreach; he is the co-founder of the Quantum Computer System (QuCS) Lecture Series, an impactful public online lecture series in the quantum computing community. He is one of the major contributors to the TorchQuantum library, which has been adopted by IBM Qiskit Ecosystem and PyTorch Ecosystem with 1.3K + stars on GitHub. He received the B.S. in Electrical Engineering from the University of Wisconsin-Madison in 2020.
# Organizer
Jin-Peng Liu 刘锦鹏
# Time
Friday, 16:00-17:30
Dec 19, 2025
# Venue
B627, Shuangqing Complex Building
#Abstract
Realizing large-scale, fault-tolerant quantum computation hinges on maintaining extremely high quantum gate fidelity, a goal challenged by the inherent hardware heterogeneity and pervasive error drift in physical qubits. Conventional calibration methods often use generalized approaches and require disruptive system downtime, making them insufficient for the demanding continuous operation of Quantum Error Correction (QEC) protocols like the Surface Code. This talk presents two distinct but complementary works that address these critical challenges by implementing Hardware-Aware and In-Situ calibration strategies.We will first discuss a hardware-aware calibration protocol designed to maximize the initial fidelity of quantum gates. This fine-grained protocol profiles individual qubit pairs to assess their unique responses to control waveform candidates. By utilizing these profiling results, we implement three distinct calibration policies and employ a graph-traversal technique to identify compatible operations, enabling highly effective parallel calibration. Experimental results on real quantum machines demonstrate that this approach achieves up to a 1.84x reduction in the median two-qubit gate error rate and up to a 25x reduction in calibration overhead, compared to the default setting on IBM Eagle Machine, proving the significant benefit of tailoring control parameters to specific hardware. Following this, we introduce QECali, a novel framework for in-situ qubit calibration specifically tailored for Surface Code QEC. Unlike traditional methods that interrupt the quantum state, QECali enables continuous drift compensation without requiring system downtime, effectively preserving the integrity of ongoing computations. This framework offers the first practical solution for continuous, non-disruptive calibration, which is essential for maintaining QEC performance over long execution times, thereby advancing current quantum processors toward the required performance for truly fault-tolerant quantum computing.
