The gist of it
The applications are planned to be made available through Rigetti’s on-demand, pay-as-you-go access model via Strangeworks. This on-demand access to Rigetti systems has been enabled by tightly integrating Rigetti’s quantum processing units with the Strangeworks platform. The integration has also resulted in higher performance of Rigetti systems on Strangeworks by enabling lower overall program latency and native Quil programming language support.
Rigetti believes its new QML applications could help advance real-world research in identifying diseases, conducting climate and weather modeling, identifying defects during manufacturing, combating financial fraud, and more. Today, Rigetti published a blog post discussing its quanvolutional neural network method and related enhancements to the performance of a typical machine learning model for identifying breast cancer and pneumonia.
“Rigetti is excited to continue its close partnership with Strangeworks and make its first reference applications available to users through their platform,” said Eric Ostby, VP of Product. “We believe that quantum machine learning applications continue to be promising candidates for quantum advantage research.”
“These new applications will accelerate businesses' ability to create valuable quantum applications by enabling them to bring Rigetti quantum capabilities to their classification, modeling, and detection problems,” said whurley, Founder and CEO of Strangeworks. “Today’s announcement deepens the partnership between Rigetti and Strangeworks, and advances Strangeworks’ goal to make access to quantum applications simple, flexible and performance-driven."
More about applications and the Strangeworks platform:
- Strangeworks Managed Applications
These applications are anticipated to be available on the Strangeworks platform, which features a rapidly growing catalog of turn-key services that are designed to make applying quantum computing technologies easier to integrate into workflows and apply to problems.
- Rigetti Quanvolutional Neural Network Method
Rigetti’s Quanvolutional Neural Network method is designed to enhance image and video analysis by adding quantum-enhanced features to an existing data set for use by classical neural networks. This method is potentially well-suited for simplifying follow-on machine learning processing, since it may require less data and fewer parameters to train the classical model.
- Rigetti Quantum Kernel Method
Rigetti’s Quantum Kernel Method is designed to assess similarities between points in a data set, which may be valuable for usage in a classification or regression model. By assessing similarities in the exponentially larger space afforded by the quantum processing unit, the output of this method could potentially be used in anomaly detection.
These applications are planned to be available on the Strangeworks platform in early 2023. The quantum kernel method is planned to be available to all users on the Strangeworks platform, while the quanvolutional neural network method is planned to be available only to select customers and partners.
Rigetti has also announced its intent to join the Strangeworks Backstage Pass program, with plans to offer up to $10,000 of sponsored credits to each approved user. Rigetti plans to prioritize users with an interest in quantum machine learning for enterprise applications, including but not limited to those interested in leveraging Rigetti’s quanvolutional neural network method. To apply for access to Rigetti through the Backstage Pass program, please visit https://strangeworks.com/backstage.