He-Liang Huang has been consistently focusing on the theory and implementation of quantum computing. In terms of addressing the challenges of scalability in quantum computing, Huang, as the lead researcher in theoretical work, played a crucial role in achieving a significant milestone in "Quantum Computational Advantage" on the 66-qubit superconducting quantum computing prototype named "Zuchongzhi”, demonstrating the quantum computer's ability to significantly surpass traditional supercomputers in solving specific problems. The processing speed for random circuit sampling problems was seven orders of magnitude faster than state of the art supercomputers and four orders of magnitude faster than Google's "Sycamore" superconducting quantum computing prototype, thereby helping China's superconducting quantum computing achieve a transformation from catching up to leading internationally. Huang developed highly efficient classical algorithms for simulating quantum circuits and, as a member of the Chinese supercomputing application team, achieved "real-time simulation of large-scale quantum random circuits" on the new Sunway supercomputer, completing a sampling task in 304 seconds that Google's "Sycamore" accomplished in 200 seconds, with higher fidelity. This achievement received the highest international award in the field of high-performance computing applications, the 2021 "Gordon Bell Prize".
In the realization of quantum computing applications, Huang’s research has spanned from fundamental questions about the accelerability of quantum computing to the design of quantum algorithms and their experimental realization. He proposed a quantum-inspired support vector machine algorithm (classical algorithm) that redefined the boundary between quantum and classical machine learning in support vector machine problems. He also designed quantum-classical hybrid convolutional neural networks, achieving remarkable breakthroughs in the field internationally. Huang is a pioneer in the experimental implementation of quantum topological data analysis, quantum machine learning for handwritten digit generation, and secure quantum cloud computing. These breakthroughs have overcome key technical challenges in the practical implementation of quantum computing intelligence and cloud-based applications.
Huang has also made significant contributions in quantum error correction. He achieved a 9- qubit Toric error-correcting code in 2019, setting a world record for the largest-scale optical planar error-correcting code. He also made breakthroughs in key technical challenges such as repeated error-correcting surface code, logical qubit quantum teleportation, enabling the scaling-up of encoding from single-digit physical qubits to double-digit scales. These achievements address the crucial technologies of scalable and practical quantum error correction.
