The fast anneal feature accelerates quantum computations, enhancing performance and promising advancements in AI, drug discovery, and material science.
D-Wave Quantum Inc., a provider of quantum computing systems, software, and services and the first commercial supplier of quantum computers globally, has introduced the fast-anneal feature across all its quantum processing units (QPUs), which are available through the Leap real-time quantum cloud service.
This feature demonstrates the advantages of annealing quantum computing over traditional algorithms for complex optimization challenges. The feature allows quantum computations at remarkable speeds and reduces the impact of external disturbances like thermal fluctuations and noise.
The fast-anneal feature is expected to attract attention from both commercial and academic researchers eager to develop applications, enhance benchmarking studies, and link increased coherence to improved performance. The company claims that the fast-anneal feature can enhance the development of quantum-powered generative AI models, accelerating drug discovery and material design by efficiently encoding complex data patterns.
“Providing direct access to Fast Anneal, which has been at the heart of D-Wave’s recent advancements, represents a significant step forward in our mission to provide customers with the resources they need to drive innovation and achieve extraordinary results,” said Dr Alan Baratz, CEO of D-Wave. “We believe it will further empower them to build industry-shaping applications with the most powerful quantum computing environment available today.”
“The ability to use the fast-anneal feature to interact with D-Wave’s Advantage2 prototype directly is fascinating for our work building quantum-enhanced generative AI models trained on molecular data to accelerate drug discovery and design new materials,” said Christopher Savoie, co-founder and CEO of Zapata AI. “The fast-anneal feature can produce coherent distributions that have the potential to allow more efficient encoding of complex data patterns in a way that is classically impractical. In addition to molecular discovery applications, this feature could be valuable in other industrial applications involving complex data patterns, particularly in combinatorial optimization problems across industries.”
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