Rigetti Brings Quantum ML Apps to Strangeworks Platform

By Carolyn Mathas

Strangeworks, Inc.  and Rigetti Computing, Inc. just announced that the Strangeworks platform will feature two new quantum machine learning (QML) applications from Rigetti. The quantum kernel and quantum convolutional "quanvolutional" neural network methods, specifically optimized for Rigetti quantum computers, target classification and regression applications development.

According to whurley (William Hurley), Strangeworks Founder and CEO, “We’re excited for what Rigetti’s proof of concept could mean for image classification and beyond. It’s early evidence of real-world quantum-driven applications resulting more accurate and efficient work, while improving momentum, confidence and saving time and money across many industries. “

Access to Rigetti systems is enabled via tight integration of Rigetti’s quantum processing units and the platform. This integration delivers the higher performance of Rigetti systems, lowers overall program latency and provides native Quil programming language support. Rigetti believes QML applications could advance real-world research including identifying diseases, conducting climate and weather modeling, identifying manufacturing defects and combating financial fraud.

Rigetti’s Quanvolutional Neural Network method enhances image and video analysis by adding quantum-based features to an existing data set used by classical neural networks. This simplifies the follow-on machine learning processing, requiring less data and fewer parameters to train the classical model. Medical imaging, for example, involves the large datasets and complex probability distributions machine learning performs. Rigetti’s method enhances the performance of a typical AI model for diagnosing breast cancer and pneumonia from the MedMNIST dataset collection. Previously, purely classical neural networks required more than 800,000 parameters to train, quantum enhanced neural networks in comparison require only 200,000, or 75% fewer parameters.

Rigetti’s Quantum Kernel Method assesses similarities between points in a data set, valuable for a classification or regression model. Assessing the similarities in the exponentially larger space afforded by the quantum processing unit, the output could be used for anomaly detection. This method will be available to all users on the Strangeworks platform, while the quanvolutional neural network method will be available to select customers and partners.

Rigetti is also joining the Strangeworks Backstage Pass program and offering up to $10,000 of sponsored credits to each approved user. Acceptance criteria involves an interview process to determine use cases and expectations, with a priority on enterprise teams interested in leveraging quantum machine learning in their work.  To apply for access to Rigetti through the Backstage Pass program, click here.

“Applying quantum capabilities to machine learning is early, obviously,” said whurley. “What we’re seeing from Rigetti is a fantastic indication that quantum can be used to improve the machine learning process. This is just the beginning. Challenges still include noise on existing quantum devices and the size of problems that can be approached. While we can’t say when these challenges will be overcome, we’re very excited to help lead the way with Rigetti,” he added.

The Strangeworks platform is designed to remove the barriers to applying emerging compute technologies, like quantum, to big problems, and make them all as easy to access, integrate, and manage as possible. Strangeworks will announce the general availability of its platform at SXSW 2023 as a pay-as-you-go service. Until then, early access is available through the Backstage Pass Program.

More information is available in a press release posted on the Strangeworks website here.

December 13, 2022