Build Instructions

1. docker build -f docker/Dockerfile.pennylane -t pytorch-pennylane
2. . ./pm_martin.source

Deep Research document for QPIE

https://deepwiki.com/gzquse/QPIE_datacircuit

CUDA-Q version

https://nvidia.github.io/cuda-quantum/latest/using/install/install.html

Visulize the data

Moon dataset

view it interactively

1. ./pl_sum.py -p a -Y

Spiral dataset

1. ./pl_sum.py -p b -Y

run with shifter

shifter --image=nersc/pytorch:24.06.01 --module gpu,nccl-plugin --env PYTHONUSERBASE=$SCRATCH/cudaq

dry run qpie

./qpie_model_inference.py

GPU requriements NVIDIA A100 80GBs

git clone https://github.com/PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning
docker build -f docker/Dockerfile --target ${TARGET} .

the other real world datasets stored in data directory