Hello, I was wondering if there is a tutorial on how to use GPU acceleration with qsim circuit simulations in Python (I have a parameterized circuit written in Cirq which is optimized in a python script, and I would like to carry out the circuit evaluations on an NVIDIA GPU). I was also wondering if there is any benchmarking data for what kind of speedup one could expect compared to regular CPU computation.

We have a tutorial for using GPU acceleration on GCP here: GPU-based quantum simulation on Google Cloud. If you have your own GPU-enabled device that you'd like to use instead, you can follow the instructions in steps 5 and 6 to locally build and verify the GPU version of qsim.

The goal of this issue (which is currently incomplete) is to remove step 5 from the process, so that any GPU-enabled device could simply run pip install qsimcirq and have the GPU version ready to go.

As for benchmarking data: #449 will add detailed info to the site, but on larger circuits (25+ qubits) we've observed GPU simulations completing ~5x faster than CPU simulations of the same circuit.

Complicating factors identified for implementing option (2) described in the first comment:

• CUDA must be installed on the runner and be exposed to the cibuildwheel Docker container. Since the image we currently use is required for cross-compatibility, we cannot exchange it for an image with CUDA pre-installed. (CUDA docs also suggest that the host machine must have a GPU to use the CUDA image, further prohibiting this approach)
• GCP scripts for installing CUDA are not applicable here, as Github Actions runners cannot be rebooted (and are likely different in several other ways).