New hybrid quantum–classical computing approach used to study chemical systems
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Jul-2025 11:11 ET (10-Jul-2025 15:11 GMT/UTC)
This work marks the first practical use of boson sampling, long seen as a key demonstration of quantum computing’s potential to outperform classical methods.
The researchers used computer simulations to model a quantum optical experiment that recognizes images using just three photons, successfully identifying images from several well-known datasets.
This paves the way towards future applications of quantum AI in complex image recognition, and represents a step toward low-resource, energy-efficient quantum computing.
A UBC Okanagan research team has developed an innovative artificial intelligence system that can accurately predict where ships are heading and arriving, potentially helping Canadian ports better prepare for incoming vessels and respond more efficiently to global supply chain disruptions.
Dr. Zheng Liu, a Professor with UBCO’s School of Engineering, and doctoral student Chengkai Zhang have created TrajReducer, a framework that increases prediction accuracy and computational efficiency by analyzing ship trajectories through advanced spatial clustering and cross-dimensional metadata ranking.
The BMFTR-funded SPINNING project has successfully demonstrated that hybrid-integrable, scalable, and near-room-temperature solid-state quantum components represent a robust and energy-efficient alternative to established quantum computing hardware platforms. The developed spin qubits in diamond outperform comparable commercially available superconducting systems in terms of longer operation times and lower error rates. Photonic coupling over distances of more than 20 meters promises to serve as a foundation for more powerful distributed quantum computers.