Tiny Quantum Nanostructures Could Make AI Less of an Energy Hog

Insider Brief

• University of Wisconsin-Madison researchers designed a theoretical quantum-emitter nanostructure that could supply the nonlinear operations needed for energy-efficient optical neural networks.
• Simulations indicated the approach could reduce the power needed for optical nonlinearity by seven orders of magnitude compared with conventional optical materials.
• The work remains computational, but the researchers said advances in diamond-based quantum photonics could make experimental implementation feasible with current technology.
• PhD student Qingyi Zhou used computational simulations to find a new way to bypass the nonlinearity bottleneck in optical neural networks. (Photo: Joel Hallberg, Story: Jason Daley)

PRESS RELEASE — Engineers at the University of Wisconsin-Madison have designed a new type of quantum nanostructure that could enable optical neural networks. This emerging technology has the potential to make artificial intelligence systems, like large language models and image generation, faster and significantly more energy efficient.

The research, led by electrical and computer engineering PhD students Qingyi Zhou and Jungmin Kim, computer science PhD student Yutian Tao, and Zongfu Yu, a professor of electrical and computer engineering, was published in the journal Nature Communications on August 27, 2026.