Customers
and partners
Building the future alongside the world's most innovative organizations.
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IonQ customers
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IonQ partners
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IonQ case studies
Accelerating drug development through enhanced simulations with AstraZeneca, AWS, and NVIDIA
Key Results:
- 20x faster time-to-solution for complex molecular simulations used in drug development than best previously published implementation
- Enabled high-accuracy modeling of transition metal catalysts critical for pharmaceutical synthesis
- Largest quantum-accelerated chemistry simulation ever performed for pharmaceutical applications
“Bringing together state-of-the-art quantum and GPU computing in hybrid workflows is the path to realizing quantum’s potential. This work represents a meaningful step towards applying quantum accelerated supercomputing to important use cases”
IonQ and Ansys demonstrate quantum outperforming classical in medical device design
Key Results:
- 12% performance improvement over classical computing in production medical device design
- Demonstrated quantum advantage on medical device optimization with 2.6M+ vertices and 40M+ edges
- First proven case of quantum acceleration in Computer-Aided Engineering workflows for life-saving medical devices
“This demonstration is a significant achievement for IonQ and the quantum computing industry as a whole. We’re showcasing one of the first cases ever where quantum computing is outperforming key classical methods, demonstrating real-world improvements for practical applications that will grow as our quantum hardware advances.”
Quantum hybrid LLM fine-tuning boosts accuracy and efficiency
Key Results:
- Improves accuracy by 3.14% and reduces energy use at scale, delivering practical quantum benefits from 46 qubits
- Successfully generated 5-channel EBSD steel microstructure images for critical engine component reliability
- First end-to-end quantum generative AI implementation for industrial materials science applications
“This work highlights how quantum computing can be strategically integrated into classical AI workflows, taking advantage of increased expressivity to enhance traditional AI LLMs in rare-data regimes. LLMs have demonstrated versatility far beyond pure ‘language’ applications, and we believe hybrid quantum-classical models are well positioned to unlock the next wave of AI capabilities.”
