Hi! I am doing a machine learning project which involves the following quantum circuit:
dev1 = qml.device("default.mixed", wires=2)
@qml.qnode(dev1)
def circuit(params_SU):
qml.QubitDensityMatrix(identity_matrix/2, 0)
qml.QubitDensityMatrix(rho, 1)
qml.SpecialUnitary(params_SU, [0, 1])
return qml.density_matrix([0])
The cost function is:
#define cost function
expected_result = rho / 2 + identity_matrix / 4
def cost(params_SU):
trace_B = circuit(params_SU)
cost_value = np.abs(trace_B - expected_result)
return float(np.sum(cost_value))
And here is the code for the training:
import jaxopt
#initialise the optimizer
stepsize = 0.001
opt = jaxopt.GradientDescent(cost, stepsize=stepsize, acceleration = True)
# set the number of steps
steps = 100
# set the initial parameter values
opt_state = opt.init_state(params_SU)
cost_list = []
iteration_num = 1
cost_value = cost(params_SU)
cost_recording_step = 10
while True:
# update the circuit parameters
params_SU, opt_state= opt.update(params_SU, opt_state)
if (iteration_num) % cost_recording_step == 0:
cost_value = cost(params_SU)
cost_list.append(cost_value)
if (iteration_num) % 10 == 0:
print("Cost after step {:5d}: {: .7f}".format(iteration_num, cost(params_SU)))
if iteration_num >= steps:
break
iteration_num += 1
However, I kept getting this error that I had no idea to resolve:
---------------------------------------------------------------------------
TracerBoolConversionError Traceback (most recent call last)
Cell In[7], line 22
18 cost_threshold = 0.1
20 while True:
21 # update the circuit parameters
---> 22 params_SU, opt_state= opt.update(params_SU, opt_state)
24 if (iteration_num) % cost_recording_step == 0:
25 cost_value = cost(params_SU)
[... skipping hidden 12 frame]
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jaxopt\_src\gradient_descent.py:97, in GradientDescent.update(self, params, state, *args, **kwargs)
82 def update(self,
83 params: Any,
84 state: NamedTuple,
85 *args,
86 **kwargs) -> base.OptStep:
87 """Performs one iteration of gradient descent.
88
89 Args:
(...)
95 (params, state)
96 """
---> 97 return super().update(params, state, None, *args, **kwargs)
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jaxopt\_src\proximal_gradient.py:305, in ProximalGradient.update(self, params, state, hyperparams_prox, *args, **kwargs)
293 """Performs one iteration of proximal gradient.
294
295 Args:
(...)
302 (params, state)
303 """
304 f = self._update_accel if self.acceleration else self._update
--> 305 return f(params, state, hyperparams_prox, args, kwargs)
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jaxopt\_src\proximal_gradient.py:264, in ProximalGradient._update_accel(self, x, state, hyperparams_prox, args, kwargs)
262 t = state.t
263 stepsize = state.stepsize
--> 264 (y_fun_val, aux), y_fun_grad = self._value_and_grad_with_aux(y, *args,
265 **kwargs)
266 next_x, next_stepsize = self._iter(iter_num, y, y_fun_val, y_fun_grad,
267 stepsize, hyperparams_prox, args, kwargs)
268 next_t = 0.5 * (1 + jnp.sqrt(1 + 4 * t ** 2))
[... skipping hidden 8 frame]
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jaxopt\_src\base.py:71, in _add_aux_to_fun.<locals>.fun_with_aux(*a, **kw)
70 def fun_with_aux(*a, **kw):
---> 71 return fun(*a, **kw), None
Cell In[6], line 6
4 def cost(params_SU):
5 #trace_A = q_obj.ptrace(0)
----> 6 trace_B = circuit(params_SU)
7 trace_B = np.abs(trace_B)
8 #cost_value = np.abs(np.array(trace_A + trace_B - 2 * expected_result))
9 #cost_value = np.abs(np.array(trace_B - expected_result))
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\qnode.py:1027, in QNode.__call__(self, *args, **kwargs)
1022 full_transform_program._set_all_argnums(
1023 self, args, kwargs, argnums
1024 ) # pylint: disable=protected-access
1026 # pylint: disable=unexpected-keyword-arg
-> 1027 res = qml.execute(
1028 (self._tape,),
1029 device=self.device,
1030 gradient_fn=self.gradient_fn,
1031 interface=self.interface,
1032 transform_program=full_transform_program,
1033 config=config,
1034 gradient_kwargs=self.gradient_kwargs,
1035 override_shots=override_shots,
1036 **self.execute_kwargs,
1037 )
1039 res = res[0]
1041 # convert result to the interface in case the qfunc has no parameters
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\interfaces\execution.py:616, in execute(tapes, device, gradient_fn, interface, transform_program, config, grad_on_execution, gradient_kwargs, cache, cachesize, max_diff, override_shots, expand_fn, max_expansion, device_batch_transform)
614 # Exiting early if we do not need to deal with an interface boundary
615 if no_interface_boundary_required:
--> 616 results = inner_execute(tapes)
617 return post_processing(results)
619 _grad_on_execution = False
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\interfaces\execution.py:249, in _make_inner_execute.<locals>.inner_execute(tapes, **_)
247 if numpy_only:
248 tapes = tuple(qml.transforms.convert_to_numpy_parameters(t) for t in tapes)
--> 249 return cached_device_execution(tapes)
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\interfaces\execution.py:371, in cache_execute.<locals>.wrapper(tapes, **kwargs)
366 return (res, []) if return_tuple else res
368 else:
369 # execute all unique tapes that do not exist in the cache
370 # convert to list as new device interface returns a tuple
--> 371 res = list(fn(tuple(execution_tapes.values()), **kwargs))
373 final_res = []
375 for i, tape in enumerate(tapes):
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\contextlib.py:81, in ContextDecorator.__call__.<locals>.inner(*args, **kwds)
78 @wraps(func)
79 def inner(*args, **kwds):
80 with self._recreate_cm():
---> 81 return func(*args, **kwds)
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\_qubit_device.py:460, in QubitDevice.batch_execute(self, circuits)
455 for circuit in circuits:
456 # we need to reset the device here, else it will
457 # not start the next computation in the zero state
458 self.reset()
--> 460 res = self.execute(circuit)
461 results.append(res)
463 if self.tracker.active:
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\devices\default_mixed.py:685, in DefaultMixed.execute(self, circuit, **kwargs)
683 wires_list.append(m.wires)
684 self.measured_wires = qml.wires.Wires.all_wires(wires_list)
--> 685 return super().execute(circuit, **kwargs)
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\_qubit_device.py:279, in QubitDevice.execute(self, circuit, **kwargs)
276 self.check_validity(circuit.operations, circuit.observables)
278 # apply all circuit operations
--> 279 self.apply(circuit.operations, rotations=self._get_diagonalizing_gates(circuit), **kwargs)
281 # generate computational basis samples
282 if self.shots is not None or circuit.is_sampled:
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\devices\default_mixed.py:699, in DefaultMixed.apply(self, operations, rotations, **kwargs)
693 raise DeviceError(
694 f"Operation {operation.name} cannot be used after other Operations have already been applied "
695 f"on a {self.short_name} device."
696 )
698 for operation in operations:
--> 699 self._apply_operation(operation)
701 # store the pre-rotated state
702 self._pre_rotated_state = self._state
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\devices\default_mixed.py:604, in DefaultMixed._apply_operation(self, operation)
601 return
603 if isinstance(operation, QubitDensityMatrix):
--> 604 self._apply_density_matrix(operation.parameters[0], wires)
605 return
607 if isinstance(operation, Snapshot):
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\pennylane\devices\default_mixed.py:540, in DefaultMixed._apply_density_matrix(self, state, device_wires)
537 if dm_dim != state.shape[0]:
538 raise ValueError("Density matrix must be of length (2**wires, 2**wires)")
--> 540 if not qnp.allclose(
541 qnp.trace(qnp.reshape(state, (state_dim, state_dim))), 1.0, atol=tolerance
542 ):
543 raise ValueError("Trace of density matrix is not equal one.")
545 if len(device_wires) == self.num_wires and sorted(device_wires.labels) == list(
546 device_wires.labels
547 ):
548 # Initialize the entire wires with the state
[... skipping hidden 1 frame]
File c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jax\_src\core.py:1510, in concretization_function_error.<locals>.error(self, arg)
1509 def error(self, arg):
-> 1510 raise TracerBoolConversionError(arg)
TracerBoolConversionError: Attempted boolean conversion of traced array with shape bool[]..
The error occurred while tracing the function update at c:\Users\User\AppData\Local\Programs\Python\Python311\Lib\site-packages\jaxopt\_src\gradient_descent.py:82 for jit. This value became a tracer due to JAX operations on these lines:
operation a:c128[] = convert_element_type[new_dtype=complex128 weak_type=False] b
from line C:\Users\User\AppData\Local\Temp\ipykernel_25644\952782401.py:3:27 (circuit)
operation a:c128[2,2] = div b c
from line C:\Users\User\AppData\Local\Temp\ipykernel_25644\952782401.py:3:27 (circuit)
operation a:bool[] = pjit[
name=allclose
jaxpr={ lambda ; b:c128[] c:f64[] d:f64[] e:f64[]. let
f:bool[] = pjit[
name=isclose
jaxpr={ lambda ; g:c128[] h:f64[] i:f64[] j:f64[]. let
k:c128[] = convert_element_type[
new_dtype=complex128
weak_type=False
] h
l:f64[] = convert_element_type[new_dtype=float64 weak_type=False] i
m:f64[] = convert_element_type[new_dtype=float64 weak_type=False] j
n:c128[] = sub g k
o:f64[] = abs n
p:f64[] = abs k
q:f64[] = mul l p
r:f64[] = add m q
s:bool[] = le o r
t:bool[] = pjit[
name=isinf
jaxpr={ lambda ; u:c128[]. let
v:f64[] = real u
w:f64[] = imag u
x:f64[] = abs v
y:bool[] = eq x inf
z:f64[] = abs w
ba:bool[] = eq z inf
bb:bool[] = or y ba
in (bb,) }
] g
bc:bool[] = pjit[
name=isinf
jaxpr={ lambda ; u:c128[]. let
v:f64[] = real u
w:f64[] = imag u
x:f64[] = abs v
y:bool[] = eq x inf
z:f64[] = abs w
ba:bool[] = eq z inf
bb:bool[] = or y ba
in (bb,) }
] k
bd:bool[] = or t bc
be:bool[] = and t bc
bf:bool[] = not bd
bg:bool[] = and s bf
bh:bool[] = eq g k
bi:bool[] = and be bh
bj:bool[] = or bg bi
bk:bool[] = ne g g
bl:bool[] = ne k k
bm:bool[] = or bk bl
bn:bool[] = not bm
bo:bool[] = and bj bn
in (bo,) }
] b c e d
bp:bool[] = reduce_and[axes=()] f
in (bp,) }
] bq br bs bt
from line C:\Users\User\AppData\Local\Temp\ipykernel_25644\192657765.py:6:14 (cost)
See https://jax.readthedocs.io/en/latest/errors.html#jax.errors.TracerBoolConversionError
It would be very kind if anyone can give offer some suggestions and resolutions. I have been stuck on this for quite a while 
Thank you so much!!! Here is the output of qml.about().
Name: PennyLane
Version: 0.33.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/PennyLaneAI/pennylane
Author:
Author-email:
License: Apache License 2.0
Location: c:\Users\86986\AppData\Local\Programs\Python\Python311\Lib\site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-Lightning
Platform info: Windows-10-10.0.22631-SP0
Python version: 3.11.4
Numpy version: 1.26.4
Scipy version: 1.11.2
Installed devices:
- default.gaussian (PennyLane-0.33.1)
- default.mixed (PennyLane-0.33.1)
- default.qubit (PennyLane-0.33.1)
- default.qubit.autograd (PennyLane-0.33.1)
- default.qubit.jax (PennyLane-0.33.1)
- default.qubit.legacy (PennyLane-0.33.1)
- default.qubit.tf (PennyLane-0.33.1)
- default.qubit.torch (PennyLane-0.33.1)
- default.qutrit (PennyLane-0.33.1)
- null.qubit (PennyLane-0.33.1)
- lightning.qubit (PennyLane-Lightning-0.33.1)