D-Wave Quantum Inc., a leader in quantum computing systems, software and services, today announced the release of a collection of open-source tools for developers that advance quantum computing integration with machine learning technology.

Available for download now,  a new quantum AI toolkit will allow developers to use the company’s quantum processors to build AI applications with the popular PyTorch framework, a popular machine learning system used to develop and train deep learning models.

The toolkit includes a neural network module for using a quantum computer to build and train machine learning models known as restricted Boltzmann machines. RBMs are used for generative AI tasks, including image recognition and drug discovery. Training these models with large datasets can be very costly and time-consuming for classical computers. However, offloading the process to a quantum computer can significantly reduce the required time and energy.

D-Wave said that by integrating PyTorch with its annealing quantum technology, companies can greatly accelerate the speed and reduce the cost of training new 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.

The company included a demo alongside the quantum AI tools that showcases the use of hybrid quantum-classical machine learning using generative AI to produce simple images. The demo uses quantum training from a digit dataset to generate images of hand-written numbers. Although a simplistic demonstration, it illustrates how developers can use the toolkit to experiment with using quantum processors to train machine learning algorithms.

“Customers are increasingly asking us for ways to facilitate the exploration of quantum and AI, recognizing the collaborative potential of these two complementary technologies,” added Lanting.

D-Wave’s quantum computing architecture and AI products have already been put to real-world use. For example, D-Wave and the pharmaceutical division of Japan Tobacco Inc. partnered in April to use quantum technology and AI to accelerate drug discovery by building a proof-of-concept AI model. According to the two companies, D-Wave’s quantum computing technology allowed the generation of a proof-of-concept that significantly outperformed classical methods for AI training.

Researchers at Jülich Supercomputing Centre at Forschungszentrum in Jülich, Germany, are using D-Wave’s quantum technology to develop a machine learning tool to predict protein-DNA binding with greater accuracy than traditional methods that use classical computing. Canada’s particle accelerator center, TRIUMF, and its partners recently published a paper on simulating high-energy particle interactions using quantum computers, where AI models were used to create synthetic data.

The new toolkit is part of D-Wave’s open-source Ocean software suite and is available starting today. It includes a software development kit and Python source code tools on GitHub. Organizations interested in exploring the integration between quantum computing and AI can sign up for the company’s Quantum LaunchPad program to get a three-month trial to test out hybrid-quantum applications.

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