Bo-Han Wu received his B.S. degree in Electrophysics from National Chiao Tung University in Taiwan in 2011, his M.S. degree in Physics from National Tsing Hua University in Taiwan in 2014, and his Ph.D. in Physics from the University of Arizona in 2022. His doctoral experimental research advanced continuous-variable (CV) quantum photonic information processing by proposing scalable, CMOS-compatible photonic platforms for generating high-dimensional cluster states on silicon nitride (SiN) chips. Additionally, his theoretical contributions during his doctoral studies significantly advanced quantum error correction (QEC) methodologies for CV systems and quantum radar techniques, achieving enhanced detection sensitivity through quantum entanglement and pulse-compression methods.

In 2023, Dr. Wu joined the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT) as a postdoctoral researcher. In this position, he expanded his research to integrate quantum systems with machine learning, pioneering the Quantum Neuromorphic Sensor Network (QNSN), a physics-inspired neural network sensor that notably improves signal processing efficiency and enhances signal-to-noise ratios compared to conventional sensing methods. He also led research efforts on long-distance and high-speed quantum key distribution, employing single-photon sources and incoherent atomic absorption to offer robust, efficient alternatives to conventional quantum communication protocols.

Dr. Wu’s research vision focuses on quantum machine learning, emphasizing a co-design approach that integrates quantum information science with machine learning methodologies. As a theorist dedicated to bridging theoretical concepts with experimental implementation, his future research directions include developing entangled neuromorphic networks, advanced machine-learning-assisted QEC techniques, physics-informed neural networks (PINNs), variational quantum circuits (VQCs) for complex quantum state preparation, and experiment quantum integrated photonic platforms, such as on-chip optical squeezers. By leveraging machine learning to unlock new quantum optics experiment and information processing theory, Dr. Wu aims to advance scalable, efficient, and experimental quantum technologies across computing, communication, and sensing applications.