Gang Huang and Yilun Xu, who are a staff scientist and a research scientist, respectively, from the Berkeley Accelerator Controls and Instrumentation (BACI) Program in the Accelerator Technology & Applied Physics (ATAP) Division at Lawrence Berkeley National Laboratory (Berkeley Lab), have been named the Physical Science Area’s “Inventors/Developers of the Year” by the Lab’s Intellectual Property Office (IPO). This award honors the most prolific inventors and software developers in each Area of the Lab based on the number of disclosures submitted to the IPO.
The award, presented at an IPO-hosted ceremony on January 22, 2025, recognizes their contributions to developing innovative control systems that enhance quantum computing performance.
“Receiving this award is an incredible honor and a testament to our team’s hard work and dedication,” said Huang. “It validates our efforts to push the boundaries of quantum computing and develop advanced, cutting-edge control technologies. This recognition inspires us to continue innovating and emphasizes the importance of our contributions to the scientific community. It is truly gratifying to see our work recognized this way, and it motivates us to pursue even greater advancements in the future.”
According to Xu, the award is a recognition of their contribution to new and efficient control techniques for quantum computing:
- Control hardware, comprising a series of compact, modular, and cost-effective boards to perform signal conditioning for radio-frequency pulses used to control and measure quantum bits (qubits).
- A hardware-efficient protocol for performing randomized compiling that generates the twirling gates and correction gates on the field-programmable gate array (FPGA) in the quantum control system.
- A hardware-software co-design technique where software identifies unique structures in a circuit and performs circuit peeling to reduce the compilation time and a hardware architecture that performs real-time circuit stitching.
- FPGA-based machine learning for real-time quantum state discrimination leveraging an integrated multi-layer neural network on the FPGA platform (RFSoC) to perform state discrimination directly within the control hardware, eliminating the need for data transfer to host computers.
(l-r) Qing Ji, staff scientist and BACI Program head, Yilun Xu, Gang Huang, and the Intellectual Property Office’s Lucian Sweitzer. (Credit: Robinson Kuntz/Berkeley Lab)
Xu says that as the developers of these inventions, “Gang and I identified the initial challenges and opportunities in quantum computing control technologies and created innovative solutions. We designed the hardware architectures, developed efficient protocols, and integrated machine learning algorithms on FPGA platforms. We successfully translated theoretical concepts into functional, real-world applications that advance the field of quantum computing.”
The researchers thanked BACI’s QubiC team for their “tremendous effort,” the valuable technical discussions with members of the Quantum Nanoelectronics Laboratory at the University of California, Berkeley, and support from Berkeley Lab’s Physical Sciences Area, ATAP, BACI, Computing Sciences Area, the Applied Mathematics & Computational Research (AMCR) Division, AMR’s Quantum Information Science & Technology group, the Quantum Systems Accelerator (QSA), a Department of Energy (DOE) National Quantum Information Science Research Center funded by the DOE Office of Science, led by Berkeley Lab and center lead partner Sandia National Laboratories, and the Advanced Quantum Testbed (AQT), a DOE Office of Science Advanced Scientific Computing Research program.
The research that underpins the inventions received funding from the QSA, AQT, and BACI.
To learn more …
Machine Learning Accelerates Progress Toward Scalable Quantum Computers
Controlling and Measuring Qubits
For more information on ATAP News articles, contact caw@lbl.gov.
