Quantum technologies, spanning sensing, communication, and computing, are poised to revolutionize modern technology by unlocking unprecedented capabilities. However, these systems face significant challenges, particularly the lack of comprehensive system-level optimization. Traditional approaches treat hardware and information processing as separate components, making quantum devices susceptible to noise and operational inefficiencies. Machine learning (ML) enables real-time adaptive optimization of complex quantum systems by extracting critical signal features, suppressing noise, and enhancing performance directly at the physical layer. This integration unifies hardware operation and data processing, fostering dynamic and resilient quantum architectures. In this talk, I will introduce three representative works [1-3]: quantum radar, quantum repeater, and cluster state generation—each addressing key challenges in quantum sensing, communication, and computing with photons. I will then present my research on the Neuromorphic RF sensor, a physics-inspired AI model that leverages ML in RF sensing optimization. By incorporating the underlying physics of the micro-ring (MiR) enabled physical encoder, we create a high-performance sensing platform based on the neuromorphic scheme. Looking ahead, the co-design of quantum hardware and ML promises breakthroughs in secure communication, high-precision sensing, and quantum computing by uncovering non-intuitive design efficiencies that transform experimental setups into deployable systems, laying the foundation for next-generation quantum infrastructures.

[1] Phys. Rev. Research 2. 023138 (2020)

[2] Quantum Sci. Technol. 7. 025018 (2022)

[3] Quantum Sci. Technol. 8. 035016 (2023)

Bo-Han Wu, Ph.D., is a postdoctoral researcher at the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). His research focuses on neuromorphic computing and RF-photonic quantum sensing. Dr. Wu earned his B.S. in Electrophysics from National Chiao Tung University (now National Yang Ming Chiao Tung University) in 2011 and an M.S. in Physics from National Tsing Hua University in 2014, where he studied four-wave mixing theory in cold atom systems (Rb atoms). He then worked as a research assistant (e.g., quantum memory in Cs atoms) at the Institute of Atomic and Molecular Sciences, Academia Sinica, before pursuing his Ph.D. in Physics at the University of Arizona. He received his Ph.D. in 2022, focusing on experimental and theoretical studies of continuous-variable quantum entanglement in integrated photonics and its applications in quantum computing, quantum sensing, and quantum communication. At MIT, Dr. Wu is developing physics-inspired AI models for photonic and quantum technologies, integrating machine learning with quantum systems to enhance signal processing and system optimization. His work aims to bridge quantum hardware and artificial intelligence, advancing next-generation quantum information science and technology.