Hello @isaacdevlugt. I have another question in regards to this.
I left the variable as phi to do parameter broadcasting in the following code
# Defining the quantum circuit
def U1(params, L, num_trotter_steps = 10): # We didn't include num_trotter_steps and it seemed to work
# n_js = L
# n_ws = L
# n_theta_xs = n_wires * L
# n_theta_ys = n_wires * L
start_index = 0
n_params_in_layer = (2 + 2) * L
for i in range(L):
params = params[start_index:start_index + n_params_in_layer]
H = H_perceptron
qml.evolve(perceptron.H)(params[:2], t)
for j in range(n_wires): # Single-qubit X rotations
qml.RX(params[2], wires=j)
for k in range(n_wires): # Single-qubit Y rotations
qml.RY(params[3], wires=k)
start_index += n_params_in_layer
def get_Sy(n_wires, phi):
S_0 = n_wires/2
c = 0
for i in range(n_wires):
c += (phi/(2*S_0))*qml.PauliY(wires=i)
return c
@qml.qnode(dev, interface='jax')
def circuit(params, phi = 0.001, L = 1):
for i in range(n_wires): # Making the initial CSS
qml.Hadamard(wires=i)
U1(params, L)
qml.Barrier()
for i in range(n_wires): # Perturbation
qml.RY(phi, wires = i)
qml.Barrier()
qml.adjoint(U1)(params, L)
return qml.expval(get_Sy(n_wires, phi))
def my_model(params, phi, L):
return circuit(params, phi = phi, L = L)
def loss_fn(param_vector, targets, phi, L):
param_list = perceptron.vector_to_hamiltonian_parameters(param_vector)
predictions = my_model(param_list, phi, L)
loss = jnp.sum((targets - predictions) ** 2 / len(targets))
return loss
random_seed = 25
params = perceptron.get_random_parameter_vector(random_seed)
opt = optax.adam(learning_rate=0.3)
opt_state = opt.init(params)
def update_step(params, opt_state, targets, phi, L):
loss_val, grads = jax.value_and_grad(loss_fn)(params, targets, phi, L)
updates, opt_state = opt.update(grads, opt_state)
params = optax.apply_updates(params, updates)
return params, opt_state, loss_val
phis_train = jnp.linspace(-0.005,0.005,100)
loss_history = []
for i in range(100):
params, opt_state, loss_val = update_step(params, opt_state, phis_train, phis_train, L)
if i % 5 == 0:
print(f"Step: {i} Loss: {loss_val}")
loss_history.append(loss_val)
Now, when I try to optimize my circuit I get the error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/.../.ipynb Cell 19 line 8
5 loss_history = []
7 for i in range(100):
----> 8 params, opt_state, loss_val = update_step(params, opt_state, phis_train, phis_train, L)
10 if i % 5 == 0:
11 print(f"Step: {i} Loss: {loss_val}")
/Users/.../.ipynb Cell 19 line 5
4 def update_step(params, opt_state, targets, phi, L):
----> 5 loss_val, grads = jax.value_and_grad(loss_fn)(params, targets, phi, L)
6 updates, opt_state = opt.update(grads, opt_state)
7 params = optax.apply_updates(params, updates)
[... skipping hidden 8 frame]
/Users/.../.ipynb Cell 19 line 9
7 def loss_fn(param_vector, targets, phi, L):
8 param_list = perceptron.vector_to_hamiltonian_parameters(param_vector)
----> 9 predictions = my_model(param_list, phi, L)
10 loss = jnp.sum((targets - predictions) ** 2 / len(targets))
11 return loss
/Users/.../.ipynb Cell 19 line 4
3 def my_model(params, phi, L):
----> 4 return circuit(params, phi = phi, L = L)
File ~/.../pennylane/qnode.py:970, in QNode.__call__(self, *args, **kwargs)
967 kwargs["shots"] = _get_device_shots(self._original_device)
969 # construct the tape
--> 970 self.construct(args, kwargs)
972 cache = self.execute_kwargs.get("cache", False)
973 using_custom_cache = (
974 hasattr(cache, "__getitem__")
975 and hasattr(cache, "__setitem__")
976 and hasattr(cache, "__delitem__")
977 )
File ~/.../pennylane/qnode.py:856, in QNode.construct(self, args, kwargs)
853 self.interface = qml.math.get_interface(*args, *list(kwargs.values()))
855 with qml.queuing.AnnotatedQueue() as q:
--> 856 self._qfunc_output = self.func(*args, **kwargs)
858 self._tape = QuantumScript.from_queue(q, shots)
860 params = self.tape.get_parameters(trainable_only=False)
/Users/.../.ipynb Cell 19 line 2
25 qml.Barrier()
27 qml.adjoint(U1)(params, L)
---> 29 return qml.expval(get_Sy(n_wires, phi))
File ~/.../pennylane/measurements/expval.py:61, in expval(op)
58 if isinstance(op, MeasurementValue):
59 return ExpectationMP(obs=op)
---> 61 if not op.is_hermitian:
62 warnings.warn(f"{op.name} might not be hermitian.")
64 return ExpectationMP(obs=op)
File ~/.../pennylane/ops/op_math/sum.py:176, in Sum.is_hermitian(self)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
File ~/.../pennylane/ops/op_math/sum.py:176, in <genexpr>(.0)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
File ~/.../pennylane/ops/op_math/sum.py:176, in Sum.is_hermitian(self)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
File ~/.../pennylane/ops/op_math/sum.py:176, in <genexpr>(.0)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
[... skipping similar frames: <genexpr> at line 176 (1 times), Sum.is_hermitian at line 176 (1 times)]
File ~/.../pennylane/ops/op_math/sum.py:176, in Sum.is_hermitian(self)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
File ~/.../pennylane/ops/op_math/sum.py:176, in <genexpr>(.0)
173 if not math.is_abstract(coeffs_list[0]):
174 return not any(math.iscomplex(c) for c in coeffs_list)
--> 176 return all(s.is_hermitian for s in self)
File ~/.../pennylane/ops/op_math/sprod.py:193, in SProd.is_hermitian(self)
189 @property
190 def is_hermitian(self):
191 """If the base operator is hermitian and the scalar is real,
192 then the scalar product operator is hermitian."""
--> 193 return self.base.is_hermitian and not qml.math.iscomplex(self.scalar)
File ~/.../jax/_src/array.py:261, in ArrayImpl.__bool__(self)
258 def __bool__(self):
259 # deprecated 2023 September 18.
260 # TODO(jakevdp) change to warn_on_empty=False
--> 261 core.check_bool_conversion(self, warn_on_empty=True)
262 return bool(self._value)
File ~/.../jax/_src/core.py:625, in check_bool_conversion(arr, warn_on_empty)
622 raise ValueError("The truth value of an empty array is ambiguous. Use "
623 "`array.size > 0` to check that an array is not empty.")
624 if arr.size > 1:
--> 625 raise ValueError("The truth value of an array with more than one element is "
626 "ambiguous. Use a.any() or a.all()")
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
This was working fine when phi was just a scalar. But now, since it is an array, it is not working. It has to do with my get_Sy function. Thank you!
here’s an example: