D-Wave Introduces New Developer Tools to Advance Quantum AI Exploration And Innovation

Insider Brief

• D-Wave released a new open-source toolkit that lets developers use quantum computers to help train AI models, focusing on machine learning tasks like image generation and pattern recognition.
• The toolkit, part of D-Wave’s Ocean™ software suite, integrates directly with PyTorch and supports training restricted Boltzmann machines (RBMs) on D-Wave’s annealing quantum processors.
• Early collaborations with Japan Tobacco Inc., Jülich Supercomputing Centre, and TRIUMF show promising results, including improved AI training speed and accuracy over classical methods in drug discovery, protein-DNA analysis, and high-energy physics simulation.

PRESS RELEASE — D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave”), a leader in quantum computing systems, software, and services, announced today that it has released a collection of offerings to help developers explore and advance quantum artificial intelligence (AI) and machine learning (ML) innovation, including an open-source quantum AI toolkit and a demo. Available now for download, the quantum AI toolkit enables developers to seamlessly integrate quantum computers into modern ML architectures. The demo illustrates how developers can leverage this toolkit to experiment with using D-Wave™ quantum processors to generate simple images, reflecting what D-Wave believes is a pivotal step in the development of quantum AI capabilities.

By releasing this new set of tools, D-Wave aims to help organizations accelerate the use of annealing quantum computers in a growing set of AI applications. The quantum AI toolkit, part of D-Wave’s Ocean™ software suite, provides direct integration between D-Wave’s quantum computers and PyTorch, a production-grade ML framework widely used to build and train deep learning models. The toolkit includes a PyTorch neural network module for using a quantum computer to build and train ML models known as a restricted Boltzmann machine (RBM).

Used to learn patterns and connections from complex data sets, RBMs are employed for generative AI tasks such as image recognition and drug discovery. Training RBMs with large datasets can be a computationally complex and time-consuming task that could be well-suited for a quantum computer. By integrating with PyTorch, D-Wave’s new toolkit aims to make it easy for developers to experiment with quantum computing to address computational challenges in training AI models.

“With this new toolkit and demo, D-Wave is enabling developers to build architectures that integrate our annealing quantum processors into a growing set of ML models,” said Dr. Trevor Lanting, chief development officer at D-Wave. “Customers are increasingly asking us for ways to facilitate the exploration of quantum and AI, recognizing the collaborative potential of these two complementary technologies.”

Turning Quantum AI Potential into Real-World Value

D-Wave continues to advance its quantum AI product roadmap, delivering new solutions to customers while expanding development efforts. The company is working with a growing number of customers on exploratory quantum AI projects including:

• Japan Tobacco Inc.: D-Wave has completed a joint proof-of-concept project with the pharmaceutical division of Japan Tobacco Inc. (JT) that used D-Wave’s quantum computing technology and AI in the drug discovery process. The quantum proof-of-concept outperformed classical methods for AI model training in drug discovery.
• Jülich Supercomputing Centre at Forschungszentrum Jülich: Researchers used D-Wave’s quantum technology to develop a ML tool that predicts protein-DNA binding with greater accuracy than traditional methods using classical computers. The team integrated quantum computing with support vector machines to achieve improved results in various metrics, significantly enhancing classification performance.
• TRIUMF: Canada’s particle accelerator center and its partner institutions recently published a paper in npj Quantum Information showing significant speedups using D-Wave’s quantum computers over classical approaches for simulating high-energy particle-calorimeter interactions—potentially leading to major efficiencies where the AI model is used to create synthetic data.

Organizations looking to explore the integration of quantum computing into AI workloads can apply to the Leap Quantum LaunchPad™ program.

Kevin Chern, Senior Benchmarking Researcher at D-Wave, will showcase the toolkit and demo during his presentation titled “An Introduction to Quantum Annealers in Optimization and Machine Learning,” at The AI Research Summit at Ai4 2025, on August 13, 2025 from 11:05 a.m. to 11:25 a.m. PT.