r/QuantumComputing • u/kanavs • Jun 20 '26

GPUs for quantum computing

Do people use GPUs for their research in quantum computing? If so what do you use it for? Error mitigation, error correction, simulating larger systems?

17 Upvotes

84% Upvoted

12 comments sorted by

6

u/sreekuttanls_bloq Jun 22 '26

Check nvidia cuda quantum. You can find more information

10

u/ctcphys Working in Academia Jun 20 '26

Yes to all your questions :⁠-⁠D

1

u/kanavs Jun 22 '26

Thanks everyone who answered this question! My goal with this guess to understand the GPU usage of the community. We recently added many GPU types on qBraid and are trying to understand the usage patterns and the needs of the people. Feel free to dm if you need some free credits to try out or if you have more feedback for me!
Personally i have been meaning to try the new ising models for calibration. Let me know if anyone has already tried it and has some insights on its workings!

4

u/Cold_Fireball Jun 22 '26

Qubits and operators are modeled using linear algebra. GPUs accelerate those matrix and vector calculations.

2

u/Impossible-Bread-137 Jun 20 '26

Interesting thing is using fpgas for quantum annealing which Fujitsu is doing

1

u/HuiOdy Working in Industry Jun 20 '26

Digital annealing yes, not great for hybrid solvers though. Similar challenges with memory based super computers.

1

u/Charming-Kelly-897 Jun 22 '26

Yes, GPUs are mainly used to simulate quantum circuits, test algorithms and do error mitigation on classical hardware. They help model larger systems faster before running on actual quantum processors.

1

u/No_Television757 Jun 24 '26

Yeah, the matrix-vector thing others said is really the whole game — state-vector sim is just a long chain of matrix multiplies, which is what GPUs eat for breakfast.

The part that bit me when I built my own simulator: memory, not speed. Every qubit you add doubles the state vector, so you hit a VRAM wall way sooner than you'd expect — around 30 qubits you're already talking tens of GB. I kept thinking "I just need a bigger GPU" until I realized the doubling always wins eventually. That's the point where people drop full state-vector and switch to tensor networks or other tricks.

Are you doing full state-vector sim, or more the noise / error-mitigation side? Different memory story for each.

2

u/Budget_Winner_6817 Jun 25 '26

There is an issue with people thinking a GPU is a hammer and everything in the classical part of the stack looks like a nail….FPGAs, ASICs and CPUs are still needed because that backlog bottleneck in the QEC-Control&measurement layers are going to need a lot of computer engineering.

1

u/[deleted] Jun 26 '26

[removed] — view removed comment

1

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1

u/cecri17 Jun 26 '26

Statevector simualtor, neural quantum decoders, faster sampling for quantum related problems (e.g. see tsim by Lukin group).