D-Wave Launches New Hybrid Solver Plug-In for Feature Selection, A Key Component of ML
PALO ALTO, Calif. and BURNABY, British Columbia, March 20, 2023 -- D-Wave Quantum Inc., a leader in quantum computing systems, software, and services and the world’s first commercial supplier of quantum computers, today introduced a new hybrid solver plug-in for feature selection as part of its focus on helping companies leverage quantum technology to streamline development of machine learning (ML) applications. D-Wave’s new hybrid solver plug-in for the Ocean SDK enables developers to more easily incorporate quantum into feature selection/ML workflows. Built to integrate seamlessly with scikit-learn, an industry-standard, state-of-the-art ML library for Python, the new hybrid solver plug-in is available today for developers to download and use in ML projects.
The launch comes at a time when companies are rapidly turning to technologies like AI and ML to navigate increasing complexity in the enterprise. According to IDC, 78% of organizations believe that AI-driven projects have significant or very significant impact on business outcomes.
“Emerging AI/ML technology for feature discovery and reuse can facilitate faster time-to-business value, synthesizing information across the enterprise,” said Kathy Lange, Research Director for IDC's AI and Automation.
The new Ocean plug-in makes it easier to use D-Wave’s hybrid solvers for the feature selection piece of ML workflows. Feature selection – a key building block of machine learning – is the problem of determining a small set of the most representative characteristics to improve model training and performance in ML. With the new plug-in, ML developers need not be experts in optimization or hybrid solving to get the business and technical benefits of both. Developers creating feature selection applications can build a pipeline with scikit-learn and then embed D-Wave’s hybrid solvers into this workflow more easily and efficiently.
“We're hearing from customers that the combination of quantum hybrid solutions with feature selection in AI/ML model training is important for accelerating business impact,” said Murray Thom, vice president of quantum business innovation at D-Wave. “This plug-in represents yet another example of how D-Wave is facilitating quantum ML workstreams and making it easy to incorporate optimization in feature selection efforts.”
By abstracting away the optimization formulations, the new plug-in helps developers to easily incorporate feature selection tools with less required development time or ramp up and faster time-to-value. Regardless of their familiarity with quantum technology, developers can get started today by signing up for the Leap quantum cloud service for free, installing the plug-in and viewing the demo and examples. Those seeking a more collaborative approach and assistance with building a production application can reach out to D-Wave directly and also explore the feature selection offering in AWS Marketplace.
For more information about using the power of hybrid quantum in feature selection and machine learning workflows, register for our upcoming webinar on April 3, 2023 at 12pm ET.
About D-Wave Quantum Inc.
D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the world’s first commercial supplier of quantum computers—and the only company building both annealing quantum computers and gate-model quantum computers. Our mission is to unlock the power of quantum computing today to benefit business and society. We do this by delivering customer value with practical quantum applications for problems as diverse as logistics, artificial intelligence, materials sciences, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling. D-Wave’s technology is being used by some of the world’s most advanced organizations, including Volkswagen, Mastercard, Deloitte, Davidson Technologies, ArcelorMittal, Siemens Healthineers, Unisys, NEC Corporation, Pattison Food Group Ltd., DENSO, Lockheed Martin, Forschungszentrum Jülich, University of Southern California, and Los Alamos National Laboratory.