IEE Awards Seed Funding for Quantum Generative Learning

The QPL is looking forward to collaborating with Prof. Zhang's group on algorithm/hardware co-design for quantum generative learning with integrated photonics.

October 14, 2020

IEE Awards Seed Funding to Three High-Impact Efficiency Projects

From UCSB's College of Engineering:

A partnership between electrical and computer engineering assistant professors Galan Moody and Zheng Zhang will unite two emerging fields, quantum engineering and artificial intelligence (AI). Their project, “Algorithm/ Hardware Co-Design of Energy Efficient Quantum Generative Learning with Integrated Photonics,” could significantly improve the efficiency of data-intensive, and power-consuming AI tasks. 

“The seed funding kickstarts a collaboration with Professor Zhang’s research group that wouldn’t really be possible otherwise. Being a newer faculty member on campus, this kind of interdisciplinary and collaborative research that is supported by the college of Engineering and IEE is exactly what made UCSB so appealing to me,” said Moody, who joined UCSB’s faculty last fall. “I’m excited to branch out from my field to learn more about how quantum science and engineering can impact other research communities, especially working with Professor Zhang, who’s an expert on tensor computation for big data and AI.”

The pair will create both an algorithm and hardware. Zhang’s group will develop and train a hardware-friendly quantum machine-learning model on classical computing hardware. Moody’s research group will design and simulate quantum hardware based on integrated photonics that can be used to implement Zhang’s model in the future. Recent advances in quantum information processing have made it possible to achieve a “quantum advantage” over classical computers, where significantly less time and energy are required to complete certain computational tasks.

“It is natural to ask whether quantum systems can provide a similar advantage for solving challenging high-dimensional and data-intensive AI tasks,” said Moody. “The development of quantum hardware for certain AI models could significantly improve the computation time, potentially enabling new applications including Internet of Things (IoT) distributed networks.”