Quantum AI
The AI industry has made impressive advancements but faces a computational challenge due to escalating power needs and costs. Quantum computing's integration with AI and ML could offer scalable, energy-efficient solutions to address these issues to potentially offer enhanced AI capabilities. Although in nascent stages, this integration could drive scientific innovation and provide a competitive edge.
Quantum computers potentially outperform classical computers by speeding up workflows, enabling advanced generative AI models, and optimizing processes. With ongoing developments, these systems could solve larger, complex problems faster, reduce energy consumption and make AI and ML solutions more efficient.
D-Wave's Quantum AI Development Initiative
D-Wave is focused on advancing quantum AI solutions that draw upon the optimization capabilities of annealing quantum computing to help customers build more efficient, rapid, and energy-saving AI and machine learning workloads. D-Wave’s Quantum AI development initiative is exploring how to enhance pre-training optimization and model accuracy, which could provide significant advantages for businesses and researchers alike.
“We’re seeing early evidence that annealing quantum computing could play a key role in helping AI/ML with more efficient model training, reduced energy consumption and faster time-to-solution.”
— Dr. Alan Baratz, CEO of D-Wave
Satisfying the Demand for Quantum AI Applications
Researchers in Jülich, Germany, used D-Wave’s quantum technology to develop a machine learning tool that predicts protein-DNA binding with greater accuracy than traditional methods. 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 are showing significant speed-ups 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.
Honda Innovation Lab and Tohoku University developed a method to fine-tune D-Wave’s quantum computers to generate highly accurate samples for training restricted Boltzmann machines (RBMs). This approach yielded better results than traditional algorithms.
“We are trying to exploit creatively the strengths of the quantum annealer by basically using it as a sampler in a bigger, deep learning architecture. It seems that we can attack already the problems of relevant scale for solving this problem.”
— Wojtek Fedorko, Deputy Department Head, Scientific Computing at TRIUMF
Be Part of Innovation's Next Chapter
D-Wave's annealing quantum computers are redefining standards by surpassing classical techniques in specific optimization tasks. Our scientific achievements are laying a strong foundation for expanding Quantum AI capabilities, potentially paving the way for today’s inventors and innovators to explore the full potential of generative AI.
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