Hybrid Transfer Learning on Jetson AGX Xavier (Volta GPU)

I am running the Hybrid Transfer Learning example at the following URL:

For the default settings (batch_size=8, quantum=True) timing is
as follows:

default.qubit 1.3 s per batch
lightning.qubit and lightning.gpu 2.7 s per batch

Do these seem about right?


Platform info: Linux-5.10.104-tegra-aarch64-with-glibc2.31
Python version: 3.10.4
Numpy version: 1.23.2
Scipy version: 1.9.0
Installed devices:

  • lightning.gpu (PennyLane-Lightning-GPU-0.26.0.dev0)
  • default.gaussian (PennyLane-0.25.1)
  • default.mixed (PennyLane-0.25.1)
  • default.qubit (PennyLane-0.25.1)
  • default.qubit.autograd (PennyLane-0.25.1)
  • default.qubit.jax (PennyLane-0.25.1)
  • default.qubit.tf (PennyLane-0.25.1)
  • default.qubit.torch (PennyLane-0.25.1)
  • default.qutrit (PennyLane-0.25.1)
  • lightning.qubit (PennyLane-Lightning-0.25.0)
  • qiskit.aer (PennyLane-qiskit-0.24.0)
  • qiskit.basicaer (PennyLane-qiskit-0.24.0)
  • qiskit.ibmq (PennyLane-qiskit-0.24.0)
  • qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.24.0)
  • qiskit.ibmq.sampler (PennyLane-qiskit-0.24.0)

PyTorch version is 1.12.1 without CUDA.

Hi @art,

I’m getting slightly lower numbers for default.qubit (about 0.9s).