Fail to run lightning.gpu example introduced on page: https://pennylane.ai/blog/2022/07/lightning-fast-simulations-with-pennylane-and-the-nvidia-cuquantum-sdk/

PennyLane Plugins
1

Hi, I am trying to simulate large-scale quantum circuit (e.g. 30 qubits) with Lightning.gpu device. However, I failed to run the lightning.gpu example introduced on page: Lightning-fast simulations with PennyLane and the NVIDIA cuQuantum SDK. The error information is

/home/xx/.local/lib/python3.10/site-packages/autograd/tracer.py:14: UserWarning: Output seems independent of input.
  warnings.warn("Output seems independent of input.")
Traceback (most recent call last):
  File "/home/xx/.local/lib/python3.10/site-packages/pennylane/_grad.py", line 330, in _jacobian_function
    jac = tuple(_jacobian(func, arg)(*args, **kwargs) for arg in _argnum)
  File "/home/xx/.local/lib/python3.10/site-packages/pennylane/_grad.py", line 330, in <genexpr>
    jac = tuple(_jacobian(func, arg)(*args, **kwargs) for arg in _argnum)
  File "/home/xx/.local/lib/python3.10/site-packages/autograd/wrap_util.py", line 20, in nary_f
    return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
  File "/home/xx/.local/lib/python3.10/site-packages/autograd/differential_operators.py", line 62, in jacobian
    jacobian_shape = ans_vspace.shape + vspace(x).shape
TypeError: can only concatenate list (not "tuple") to list

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/xx/Documents/quantum/MG-QAOA/lightning_gpu.py", line 32, in <module>
    jac = qml.jacobian(circuit)(weights)
  File "/home/xx/.local/lib/python3.10/site-packages/pennylane/_grad.py", line 333, in _jacobian_function
    raise ValueError(
ValueError: PennyLane has a new return shape specification that may not work well with autograd and more than one measurement. That may be the source of the error.

And the test code is as follows.

import pennylane as qml
from timeit import default_timer as timer

# To set the number of threads used when executing this script,
# export the OMP_NUM_THREADS environment variable.

# Choose number of qubits (wires) and circuit layers
wires = 20
layers = 3

# Set number of runs for timing averaging
num_runs = 5

# Instantiate CPU (lightning.qubit) or GPU (lightning.gpu) device
dev = qml.device('lightning.gpu', wires=wires)


# Create QNode of device and circuit
@qml.qnode(dev, diff_method="adjoint")
def circuit(parameters):
    qml.StronglyEntanglingLayers(weights=parameters, wires=range(wires))
    return [qml.expval(qml.PauliZ(i)) for i in range(wires)]

# Set trainable parameters for calculating circuit Jacobian
shape = qml.StronglyEntanglingLayers.shape(n_layers=layers, n_wires=wires)
weights = qml.numpy.random.random(size=shape)

# Run, calculate the quantum circuit Jacobian and average the timing results
timing = []
for t in range(num_runs):
    start = timer()
    jac = qml.jacobian(circuit)(weights)
    end = timer()
    timing.append(end - start)

print(qml.numpy.mean(timing))

The details after running qml.about() is

Name: PennyLane
Version: 0.30.0
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /home/yqia7342/.local/lib/python3.10/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml
Required-by: PennyLane-Lightning, PennyLane-Lightning-GPU, PennyLane-qiskit

Platform info:           Linux-5.15.0-58-generic-x86_64-with-glibc2.35
Python version:          3.10.6
Numpy version:           1.23.4
Scipy version:           1.9.3
Installed devices:
- default.gaussian (PennyLane-0.30.0)
- default.mixed (PennyLane-0.30.0)
- default.qubit (PennyLane-0.30.0)
- default.qubit.autograd (PennyLane-0.30.0)
- default.qubit.jax (PennyLane-0.30.0)
- default.qubit.tf (PennyLane-0.30.0)
- default.qubit.torch (PennyLane-0.30.0)
- default.qutrit (PennyLane-0.30.0)
- null.qubit (PennyLane-0.30.0)
- lightning.qubit (PennyLane-Lightning-0.30.0)
- lightning.gpu (PennyLane-Lightning-GPU-0.30.0)
- qiskit.aer (PennyLane-qiskit-0.28.0)
- qiskit.basicaer (PennyLane-qiskit-0.28.0)
- qiskit.ibmq (PennyLane-qiskit-0.28.0)
- qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.28.0)
- qiskit.ibmq.sampler (PennyLane-qiskit-0.28.0).

The version of cuquantum and cuquantum-python is 22.7.1, and my GPU is GeForce RTX 2080 Ti with CUDA12.0.

Looking forward to your advice.

Thanks!

2

Hey @Yang! In v0.30, we introduced a new return-type system. Read more about it here:

https://docs.pennylane.ai/en/stable/introduction/returns.html#troubleshooting

For your issue, I would check out the Troubleshooting section!

3

Hi @Yang

Iā€™d also suggest downgrading your CUDA version to 11.8 or equivalent, as we do not currently support CUDA 12 with lightning.gpu

The issue you reported is due to the changes in the return type system, and can be rectified for autograd by wrapping the QNode return value by qml.math.stack(...).

Let us know if that helps.

4

Hi @mlxd ,

Thank you for the advice, It run successfully after warpping the return value by qml.math.value. By the way, can Pennylane support tensor network simulation for large-scale circuits (e.g. 30 qubits)? When I simulate 30-qubit circuit with lighning.gpu, it run out of memory.

5

Hi @Yang glad to hear it is working for you. We do have tensor network backends on our roadmap, and will be releasing that support later this year. We are also releasing an MPI-backed distributed statevector implementation for GPUs in the coming weeks, which may also be of interest to you for larger scale problems.

6

Hi @mlxd ,

That is great. Thank you for the contributions of your team.

Looking forward to the new features of Pennylane.

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