Accelerating Quantum Dynamics — At Scale.
A state-of-the-art time-evolution algorithm that compresses quantum dynamics
compute costs on quantum hardware, and enables
embarrassingly parallel simulations on HPC.
In quantum computing and simulation, execution time directly constrains what questions you can ask. Faster time-evolution means more iterations per day, larger parameter sweeps, and the ability to explore regimes that were previously impractical.
For research teams and enterprise R&D, this state-of-the-art approach translates to faster time-to-insight — the difference between exploring one hypothesis or ten, between a single parameter point or a comprehensive sweep.
On HPC, the ability to parallelize quantum dynamics workloads unlocks new classes of simulations that scale efficiently with available compute resources.
If you're working on quantum computing and simulation at scale — whether on quantum hardware or HPC — we'd welcome the opportunity to discuss how this state-of-the-art approach might apply to your workloads.
We respond to technical inquiries within 24 hours.