Accelerate scientific discovery with Azure Quantum Elements

Sign up to learn more about the private preview and get the latest updates from the Azure Quantum team

Accelerate scientific discovery with Azure Quantum Elements

Sign up to learn more about the private preview and get the latest updates from the Azure Quantum team

Azure Quantum Elements is a system that boosts productivity for chemistry and materials science R&D. Researchers and product developers can screen candidates, study mechanisms, and design molecules and materials through state-of-the art computing capabilities and enterprise-grade services. Azure Quantum Elements includes simulation workflows optimized for scaling on Azure HPC clusters, AI-accelerated computing, augmented reasoning using AI, integration with quantum tools to start experimenting with existing quantum hardware, and access in the future to Microsoft’s quantum supercomputer. With Azure Quantum Elements customers will be able to:


  • Accelerate time to impact, with some customers seeing a six-month to one-week speedup from project kick-off to solution.1

  • Explore more materials, with the potential to scale from thousands of candidates to tens of millions.2

  • Speed up certain chemistry simulations by 500,000 times, effectively compressing nearly one year of research into one minute.3

  • Improve productivity with Copilot in Azure Quantum Elements to query research, visualize data, and write code.

  • Get ready for quantum computing by addressing quantum chemistry problems today with AI & HPC, while experimenting with existing quantum hardware and getting priority access to Microsoft’s quantum supercomputer in the future.

  • Save time and money by accelerating R&D pipeline and bringing innovative products to market more quickly.

  1. Source: More information.
  2. Source: Internal study based on comparison between AI-based screening versus DFT physics-based screening, 2022.
  3. Source: Internal study based on comparison between ab initio Molecular dynamics and active learning molecular dynamics, 2022-2023.

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