Quantum Simulation on NVIDIA CUDA-Q
Bring GPU acceleration to your quantum workloads — up to 40× faster, depending on the circuit, at the same fidelity you expect from Quantum Rings.
Why CUDA-Q
Built for Speed and Scale
Blazing Fast Simulation
GPU acceleration delivers dramatic speedups over CPU-only simulation.
Scales Without Compromise
Push to larger circuits and deeper depths without sacrificing accuracy.
High Fidelity & Precision
Near-perfect fidelity, now at GPU speed.
Accessible Today
Runs on NVIDIA GPUs you can access right now — no waiting.
What Is CUDA-Q?
CUDA-Q is NVIDIA's open platform for GPU-accelerated quantum-classical computing. Quantum Rings plugs into CUDA-Q so you can run large-scale simulations on NVIDIA GPUs using the tools and workflow you already know.
Performance
Simulating the Sycamore Supremacy Circuit
Time to build state and complete the first shot on the largest published circuit.
| Approach | Detail | Time |
|---|---|---|
| Google (2019) | Classical supercomputer (estimated) | 10,000 years |
| IBM (2019) | Supercomputer (estimated) | 2.5 days |
| Quantum Rings (2024) | Consumer-grade CPU | 2h 46m |
| Quantum Rings (2025) | One A100 GPU | 10 min |
Classical estimates per Google (2019) and IBM (2019); Quantum Rings CPU (2024) and A100 GPU (2025) benchmarks.
It Scales Near-Linearly
Sampling time for the 53-qubit, 20-cycle Sycamore workload as CPU parallelism increases (lower is better).
Source: Wold & Kasirajan, arXiv:2512.07311, Table 2.
Published research
Backed by Published Research
Our claims aren’t marketing — they’re published, with methods and numbers anyone can check. We simulate Google’s “quantum supremacy” circuits on accessible hardware, with the fidelity and speedups to prove it.
Resources
Get Going With CUDA-Q
Run Your Circuits at GPU Speed
Free to start — same circuits, now with CUDA-Q acceleration.
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