Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

D-Wave Launches New Hybrid Solver Plug-In for Feature Selection, A Key Component of Machine Learning

D-Wave Quantum  a leader in quantum computing systems, software, and services and the world’s first commercial supplier of quantum computers, 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.

Recommended AI: AI in Retail: Israeli Startup Hexa is Enabling Shoppers to View Products in 360 Degrees & Try Them Virtually

“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.”

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 outcomes1.

“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.1

Related Posts
1 of 39,159

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. ​

Recommended AI: Google Cloud Announces Biggest-ever Upgrade to Vertex AI

“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.

Recommended AI: Is Customer Experience Strategy Making or Breaking Your ‘Shopping Festival’ Sales?

[To share your insights with us, please write to]

Comments are closed.