D-Wave Quantum Inc. has launched a new tool to help developers use quantum technology to simplify developing machine learning applications. The hybrid solver plug-in for feature selection, part of the Ocean SDK, makes incorporating quantum into Machine Learning (ML) workflows easier. It integrates smoothly with sci-kit-learn, a popular Machine Learning library for Python, and is now available for developers to download and use in their projects.
D-Wave is a leading Canadian-based supplier of quantum computing systems, software, and services and was the first company to commercialize quantum computers. They are the first commercial supplier of quantum computers and are unique in building both annealing and gate-model quantum computers.
Their mission is to harness the power of quantum computing to benefit both businesses and society. They deliver practical quantum applications to solve various problems in logistics, artificial intelligence, materials science, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling.
Simplification of Feature Selection
“Emerging AI/ML technology for feature discovery and reuse can facilitate faster time-to-business value, synthesizing information across the enterprise.”
Kathy Lange, Research Director for IDC’s AI and Automation.
The Ocean plug-in that has been recently introduced simplifies the usage of D-Wave’s hybrid solvers for the task of feature selection in machine learning workflows. Feature selection is a crucial component of machine learning that entails identifying a concise set of the most significant attributes to enhance the training and performance of the model.
“We’re hearing from customers that the combination of quantum hybrid solutions with feature
Murray Thom, vice president of quantum business innovation at D-Wave.
selection in AI/ML model training is important for accelerating business impact. 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.”
With the latest plug-in, machine learning developers do not require expertise in optimization or hybrid solving to obtain the advantages of both fields from a technical and business perspective. Developers who build feature selection applications can construct a pipeline using sci-kit-learn and incorporate D-Wave’s hybrid solvers more smoothly and productively into their workflow.
Enhancing Machine Learning Workflows through Quantum Computing
The new plug-in significantly simplifies the integration of feature selection tools into Machine Learning workflows by eliminating the need for developers to deal with complex optimization formulations. This translates into less development time and faster time-to-value, allowing developers to focus on other aspects of the Machine Learning pipeline. Notably, this plug-in is user-friendly and highly accessible to developers with no prior knowledge of quantum technology.
In addition, D-Wave offers a collaborative approach for those seeking further assistance in building production applications. This can be done by contacting D-Wave directly or exploring the feature selection offering in AWS Marketplace. The combination of the plug-in’s ease of use and accessibility with the option for further collaboration and support demonstrates the commitment of D-Wave to enable developers to leverage quantum computing to enhance their Machine Learning workflows.
