Quantum Machine Learning

“Nature isn’t classical, damnit, so if you want to make a simulation of nature, you’d better make it quantum mechanical.” — Physicist Richard Feynman

Machine learning, while it doesn’t exactly simulate systems in nature, has the ability to learn a model of a system and predict the system’s behavior. At the same time, machine learning on classical computers has shown promise in tackling challenging scientific issues, leading to advancements in image processing for cancer detection, forecasting earthquake aftershocks, predicting extreme weather patterns, and detecting new exoplanets, with more computational power leading to ever increasing performance. As such, if quantum computers could accelerate machine learning, the potential for impact is enormous.

What is Quantum Machine Learning?

Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning. For example, we might want to find out whether quantum computers can speed up the time it takes to train or evaluate a machine learning model. On the other hand, we can leverage techniques from machine learning to help us uncover quantum error-correcting codes, estimate the properties of quantum systems, or develop new quantum algorithms.

We believe quantum computing may help solve some of the most challenging computer science problems, particularly in machine learning. If we want to cure diseases, we need better models of how they develop. If we want to create effective environmental policies, we need better models of what’s happening to our climate. And if we want to build a more useful search engine, we need to better understand spoken questions and what’s on the web so you get the best answer.

Classical computers aren’t well suited to these types of creative problems. Solving such problems can be imagined as trying to find the lowest point on a surface covered in hills and valleys. Classical computing might use what’s called “gradient descent”: start at a random spot on the surface, look around for a lower spot to walk down to, and repeat until you can’t walk downhill anymore. But all too often that gets you stuck in a “local minimum” — a valley that isn’t the very lowest point on the surface.

That’s where quantum computing comes in. It lets you cheat a little, giving you some chance to “tunnel” through a ridge to see if there’s a lower valley hidden beyond it. This gives you a much better shot at finding the true lowest point — the optimal solution.

The algorithms are not hand-coded, but learned.