Photonic qubits are widely used in optical quantum information processing. However, to date, efficient high-speed single photon sources are still difficult to make. Here we propose to use "classical" phase-randomized coherent states, combined with post-processing, to perform quantum information processing tasks. We propose two techniques, a machine-learning-based approach and a two-step analytical and numerical optimization approach, to greatly enhance the numerical precision and applicable dimensions, opening up a wide variety of applications that can be implemented with easily attainable coherent light sources and threshold detectors, such as quantum metrology, small-scale linear optical quantum computing, or quantum state preparation.

Short bio:

Dr. Wenyuan (Mike) Wang is currently an Assistant Professor at the University of Calgary. Dr. Wang received his Bachelor of Science degree from the University of Hong Kong (HKU) in 2015 and his PhD degree from the University of Toronto in 2020 under the supervision of Prof. Hoi-Kwong Lo. He worked as a Postdoctoral fellow at the Institute of Quantum Computing, University of Waterloo in the group of Prof. Norbert Lutkenhaus for a year and worked as a Research Assistant Professor at HKU during 2021-2024, before joining Nara Institute of Science and Technology (NAIST) as an Associate Professor (fixed-term) in 2024 summer. Dr. Wang joined the University of Calgary in July 2025 and also holds a Visiting Associate Professor position at NAIST.

His main research interest is quantum communication, especially the design and optimization of quantum key distribution (QKD) protocols, quantum networks, as well as the combination of novel computational techniques (e.g. machine learning and semi-definite programming) with QKD. He is also interested in expanding the quantum optical platform into fields beyond QKD, such as quantum conferencing, quantum random number generation, linear optical quantum computing, quantum metrology, and quantum machine learning.