Configuration#
Backend selection is global, not per object. set_backend() writes to a
context variable, and every object reads it once in its own __init__ and
caches the answer.
import pyqit
from pyqit.models import VQCClassifier
pyqit.set_backend("torch") # first
model = VQCClassifier(n_qubits=4) # then
Order matters and getting it wrong fails quietly. Setting the backend after you build a model leaves that model on the old one.
set_backend() raises ImportError when you ask for "torch"
without torch installed. Every torch import in the package sits behind either
this call or a runtime type check, so the one guard covers them all and the
error names its own cause instead of surfacing later as a bare
ModuleNotFoundError.
What the backend changes#
Three things fork on it. The QNode is wrapped in a qml.qnn.TorchLayer or
stored as plain pnp arrays. Training runs through Lightning or through a
PennyLane optimizer loop. Loaders come from torch.utils.data or from an
internal NumPy loader.
Seeding#
set_seed() seeds NumPy, which covers PennyLane too because
pennylane.numpy.random delegates to it, and seeds torch when it is
installed. Trainer.fit calls it before anything
stochastic runs.
Weights are drawn at construction, so reproducing them means seeding first:
pyqit.set_seed(42)
model = VQCClassifier(n_qubits=4)
Trainer(seed=...) alone covers training and diagnostics, not
initialisation. Note that this mutates global RNG state, the same contract as
Lightning’s seed_everything.