Reversing quantum information loss using lessons from machine learning

PhD student Einar Gabbassov takes inspiration from image generation models to explore quantum information recovery

By Perimeter Institute

Quantum systems are fragile. When a qubit interacts with a noisy environment, the information it carries can easily be lost, scrambled in such a way that retrieving the information is virtually impossible.

But not in all scenarios.

Einar Gabbassov, Institute for Quantum Computing, Faculty of Mathematics and Perimeter Institute PhD student, has been exploring cases where quantum information loss can be reversed, taking inspiration from image generation models like DALL-E or SORA. These are known as diffusion models. They are trained by allowing an image to gradually be turned into noise, and then they attempt to work backwards from the noise to recreate a clear picture.

“The classical forward and reverse processes used by image generation models are described by a stochastic differential equation, and this equation was derived in the 1970s. It's pretty old mathematics, later adapted to machine learning,” says Gabbassov. “In quantum mechanics, we have equations that describe the forward processes of information loss, but there are no known analogs of the quantum reverse process. I wondered whether it was possible.”

In a new paper published this week in Physical Review Research, Gabbassov derived the stochastic Schrödinger equations that describe the quantum reverse process.