Jin, Lijing
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Lijing Jin obtained his Ph.D. in theoretical physics through a joint training program between Université Grenoble Alpes and the French National Centre for Scientific Research (CNRS). He is currently the tech lead of the quantum chip design team at Guangdong–Hong Kong–Macao Greater Bay Area (Guangdong) Quantum Science Center, where his work focuses on the development of high-fidelity, scalable fluxonium-based superconducting quantum processors. His research interests include the theoretical modeling of superconducting quantum devices, electromagnetic simulation and validation methodologies, and the development of automated toolchains for quantum chip design. Previously, he was a postdoctoral researcher at the Beijing Computational Science Research Center, working on quantum optomechanics. He later joined Baidu Research as a senior researcher and led the quantum chip design team, where he was responsible for the development of superconducting quantum chips and related technologies.
Toward Scalable Two-Dimensional Fluxonium Quantum Processors: Challenges and Solutions
Fluxonium qubits combine long coherence times with strong anharmonicity, making them a promising platform for scalable superconducting quantum processors. While recent experiments have demonstrated high-fidelity operations in multi-qubit fluxonium–transmon–fluxonium (FTF) architectures, extending these systems to highly connected two-dimensional (2D) architectures remains challenging. Key obstacles include the trade-offs between coupling strength, crosstalk suppression, and qubit spacing required for scalable wiring, as well as capacitive loading that fundamentally limits achievable qubit–coupler interactions.
This talk presents a quantitative design framework for scalable 2D fluxonium quantum processors. We develop a system-level design methodology based on double-transmon couplers (DTCs) that establishes quantitative relationships between circuit design parameters and processor-level performance [1]. In parallel, an analytical framework identifies the parasitic capacitances of Josephson junctions and Josephson junction arrays as the dominant origin of capacitive loading, while revealing that optimized qubit-pad geometries can effectively mitigate this limitation [2]. Together, these results establish practical design principles for realizing ultrafast, high-fidelity two-qubit gates in highly connected 2D architectures and provide a systematic pathway toward scalable fluxonium quantum processors.
[1] Guo Xuan Chan, Wangwei Lan, Tenghui Wang, Xizheng Ma, Chunqing Deng*, Lijing Jin*. "System-Level Design of Scalable Fluxonium Quantum Processors with Double-Transmon Couplers." arXiv:2604.26373 (2026).
[2] Quan Guan, Guo Xuan Chan, Xu Dou, Chunqing Deng*, Lijing Jin*. "Capacitive Loading in Two-dimensional Fluxonium Quantum Processors." arXiv:2607.22138 (2026).