Hello! I’m trying to get the gradient values of my quantum circuit to do a Barren Plateaus analysis. I am following the following tutorial: Barren plateaus in quantum neural networks — PennyLane documentation. However, I am using the torch interface, and I am running into a TypeError when I just tweak the code a little. Additionally, I am a bit confused about how the gradient in the tutorial is being defined, because I thought that the gradient has to be defined with respect to some cost function, which is not present in the tutorial code. Incidentally, I am using a classical cost function; how would I get the gradients with respect to that?
import pennylane as qml
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Variable
# qcircuit
def rotation_layer(w):
for i in range(num_qubits):
qml.RY(w[i], wires=i)
def entangling_block(w):
for i in range(num_qubits):
qml.CZ(wires = [i, (i+1)%num_qubits])
def generator(w, num_qubits, num_layers = 3):
# if init_strategy == "uniform":
# qml.Hadamard(wires=range(num_qubits)
rotation_layer(w[:num_qubits])
for i in range(1, num_layers*2 + 1, 2):
entangling_block(w[num_qubits * (i) : num_qubits * (i+1)])
rotation_layer(w[num_qubits * (i+1) : num_qubits * (i+2)])
return qml.probs(wires=range(num_qubits))
num_qubits = 3 # arbitrary
num_layers = 3
params = np.random.uniform(0, np.pi, size=(num_layers * 2 + 1) * num_qubits)
dev = qml.device("default.qubit", wires=num_qubits)
qcircuit = qml.QNode(generator, dev, interface="torch") # Have to use torch for Pytorch based optimization
grad = qml.grad(qcircuit, argnum=0)
gradient = grad(params, num_qubits, num_layers)
Full error message below:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipykernel_120/2458307331.py in <cell line: 35>()
33 qcircuit = qml.QNode(generator, dev, interface="torch")
34 grad = qml.grad(qcircuit, argnum=0)
---> 35 gradient = grad(params, num_qubits, num_layers)
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/_grad.py in __call__(self, *args, **kwargs)
113 return ()
114
--> 115 grad_value, ans = grad_fn(*args, **kwargs)
116 self._forward = ans
117
/opt/conda/envs/pennylane/lib/python3.9/site-packages/autograd/wrap_util.py in nary_f(*args, **kwargs)
18 else:
19 x = tuple(args[i] for i in argnum)
---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
21 return nary_f
22 return nary_operator
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/_grad.py in _grad_with_forward(fun, x)
131 difference being that it returns both the gradient *and* the forward pass
132 value."""
--> 133 vjp, ans = _make_vjp(fun, x)
134
135 if not vspace(ans).size == 1:
/opt/conda/envs/pennylane/lib/python3.9/site-packages/autograd/core.py in make_vjp(fun, x)
8 def make_vjp(fun, x):
9 start_node = VJPNode.new_root()
---> 10 end_value, end_node = trace(start_node, fun, x)
11 if end_node is None:
12 def vjp(g): return vspace(x).zeros()
/opt/conda/envs/pennylane/lib/python3.9/site-packages/autograd/tracer.py in trace(start_node, fun, x)
8 with trace_stack.new_trace() as t:
9 start_box = new_box(x, t, start_node)
---> 10 end_box = fun(start_box)
11 if isbox(end_box) and end_box._trace == start_box._trace:
12 return end_box._value, end_box._node
/opt/conda/envs/pennylane/lib/python3.9/site-packages/autograd/wrap_util.py in unary_f(x)
13 else:
14 subargs = subvals(args, zip(argnum, x))
---> 15 return fun(*subargs, **kwargs)
16 if isinstance(argnum, int):
17 x = args[argnum]
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/qnode.py in __call__(self, *args, **kwargs)
845 return res
846
--> 847 res = qml.execute(
848 [self.tape],
849 device=self.device,
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/interfaces/execution.py in execute(tapes, device, gradient_fn, interface, mode, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
649 if gradient_fn == "backprop" or interface is None:
650 return batch_fn(
--> 651 qml.interfaces.cache_execute(
652 batch_execute, cache, return_tuple=False, expand_fn=expand_fn
653 )(tapes)
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/interfaces/execution.py in wrapper(tapes, **kwargs)
204 else:
205 # execute all unique tapes that do not exist in the cache
--> 206 res = fn(execution_tapes.values(), **kwargs)
207
208 final_res = []
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/interfaces/execution.py in fn(tapes, **kwargs)
129 def fn(tapes: Sequence[QuantumTape], **kwargs): # pylint: disable=function-redefined
130 tapes = [expand_fn(tape) for tape in tapes]
--> 131 return original_fn(tapes, **kwargs)
132
133 @wraps(fn)
/opt/conda/envs/pennylane/lib/python3.9/contextlib.py in inner(*args, **kwds)
77 def inner(*args, **kwds):
78 with self._recreate_cm():
---> 79 return func(*args, **kwds)
80 return inner
81
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/_qubit_device.py in batch_execute(self, circuits)
654
655 # TODO: Insert control on value here
--> 656 res = self.execute(circuit)
657 results.append(res)
658
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/devices/default_qubit_torch.py in execute(self, circuit, **kwargs)
233 )
234
--> 235 return super().execute(circuit, **kwargs)
236
237 def _asarray(self, a, dtype=None):
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/_qubit_device.py in execute(self, circuit, **kwargs)
430
431 # apply all circuit operations
--> 432 self.apply(circuit.operations, rotations=circuit.diagonalizing_gates, **kwargs)
433
434 # generate computational basis samples
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/devices/default_qubit.py in apply(self, operations, rotations, **kwargs)
267 self._debugger.snapshots[len(self._debugger.snapshots)] = state_vector
268 else:
--> 269 self._state = self._apply_operation(self._state, operation)
270
271 # store the pre-rotated state
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/devices/default_qubit.py in _apply_operation(self, state, operation)
295 return self._apply_ops[operation.base_name](state, axes, inverse=operation.inverse)
296
--> 297 matrix = self._asarray(self._get_unitary_matrix(operation), dtype=self.C_DTYPE)
298
299 if operation in diagonal_in_z_basis:
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/devices/default_qubit_torch.py in _get_unitary_matrix(self, unitary)
307 if unitary in diagonal_in_z_basis:
308 return self._asarray(unitary.eigvals(), dtype=self.C_DTYPE)
--> 309 return self._asarray(unitary.matrix(), dtype=self.C_DTYPE)
310
311 def sample_basis_states(self, number_of_states, state_probability):
/opt/conda/envs/pennylane/lib/python3.9/site-packages/pennylane/devices/default_qubit_torch.py in _asarray(self, a, dtype)
248 res = torch.cat([torch.reshape(i, (-1,)) for i in res], dim=0)
249 else:
--> 250 res = torch.as_tensor(a, dtype=dtype)
251
252 res = torch.as_tensor(res, device=self._torch_device)
/opt/conda/envs/pennylane/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py in __len__(self)
19 dtype = property(lambda self: self._value.dtype)
20 T = property(lambda self: anp.transpose(self))
---> 21 def __len__(self): return len(self._value)
22 def astype(self, *args, **kwargs): return anp._astype(self, *args, **kwargs)
23
TypeError: object of type 'numpy.complex128' has no len()
Output of qml.about():
Name: PennyLane
Version: 0.28.0
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: /opt/conda/envs/pennylane/lib/python3.9/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, retworkx, scipy, semantic-version, toml
Required-by: PennyLane-Cirq, PennyLane-Lightning, PennyLane-qiskit, pennylane-qulacs, PennyLane-SF
Platform info: Linux-5.4.209-116.367.amzn2.x86_64-x86_64-with-glibc2.31
Python version: 3.9.15
Numpy version: 1.23.5
Scipy version: 1.10.0
Installed devices:
- default.gaussian (PennyLane-0.28.0)
- default.mixed (PennyLane-0.28.0)
- default.qubit (PennyLane-0.28.0)
- default.qubit.autograd (PennyLane-0.28.0)
- default.qubit.jax (PennyLane-0.28.0)
- default.qubit.tf (PennyLane-0.28.0)
- default.qubit.torch (PennyLane-0.28.0)
- default.qutrit (PennyLane-0.28.0)
- null.qubit (PennyLane-0.28.0)
- cirq.mixedsimulator (PennyLane-Cirq-0.28.0)
- cirq.pasqal (PennyLane-Cirq-0.28.0)
- cirq.qsim (PennyLane-Cirq-0.28.0)
- cirq.qsimh (PennyLane-Cirq-0.28.0)
- cirq.simulator (PennyLane-Cirq-0.28.0)
- lightning.qubit (PennyLane-Lightning-0.28.2)
- strawberryfields.fock (PennyLane-SF-0.20.1)
- strawberryfields.gaussian (PennyLane-SF-0.20.1)
- strawberryfields.gbs (PennyLane-SF-0.20.1)
- strawberryfields.remote (PennyLane-SF-0.20.1)
- strawberryfields.tf (PennyLane-SF-0.20.1)
- 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)
- qulacs.simulator (pennylane-qulacs-0.28.0)
. The reason the tutorial tracks a single parameter’s gradient across multiple random circuits with more and more qubits is to empirically demonstrate the signature of barren plateaus: exponentially vanishing gradients with the number of qubits.
.