# Qml.expval is returning Tensor object

**URL:** <https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885>\
**Category:** PennyLane Challenges\
**Created:** [July 15, 2024, 1:00pm UTC](https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885 "2024-07-15T13:00:08Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![phani.bharath](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/phani.bharath/32/2713_2.png) [@phani.bharath](https://discuss.pennylane.ai/u/phani.bharath)\
**Post date:** [July 15, 2024, 1:00pm UTC](https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885/1 "2024-07-15T13:00:08Z")

</div>

I created a Qnode with below code  
def circuit(params, hamiltonian):  
parameters = params.reshape((LAYERS, WIRES, 3))  
qml.templates.StronglyEntanglingLayers(parameters, wires=range(WIRES))  
return qml.expval(qml.Hermitian(hamiltonian, wires = [0,1]))

I am getting below type error  
TypeError: Can’t find vector space for value expval(Hermitian(tensor([[0.86332707, 0.01671081, 0.07991447, 0.0854049],  
[0.01671081, 0.82379638, -0.07695947, 0.03131549],  
[0.07991447, -0.07695947, 0.8355417 , -0.11345916],  
[0.0854049 , 0.03131549, -0.11345916, 0.75815689]], requires\_grad=False), wires=[0, 1])) of type \<class ‘pennylane.measurements.expval.ExpectationMP’\>. Valid types are dict\_keys([\<class ‘autograd.core.SparseObject’\>, \<class ‘list’\>, \<class ‘tuple’\>, \<class ‘dict’\>, \<class ‘numpy.ndarray’\>, \<class ‘float’\>, \<class ‘numpy.float128’\>, \<class ‘numpy.float64’\>, \<class ‘numpy.float32’\>, \<class ‘numpy.float16’\>, \<class ‘complex’\>, \<class ‘numpy.complex256’\>, \<class ‘numpy.complex64’\>, \<class ‘numpy.complex128’\>, \<class ‘pennylane.numpy.tensor.tensor’\>])

Because  
circuit is returning a Tensor object like this  
expval(Hermitian(tensor([[0.86332707, 0.01671081, 0.07991447, 0.0854049],  
[0.01671081, 0.82379638, -0.07695947, 0.03131549],  
[0.07991447, -0.07695947, 0.8355417 , -0.11345916],  
[0.0854049 , 0.03131549, -0.11345916, 0.75815689]], requires\_grad=False), wires=[0, 1]))  
where my cost\_fun is looking for a scalar value

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<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [July 16, 2024, 10:08pm UTC](https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885/2 "2024-07-16T22:08:11Z")

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Hi @phani.bharath ,

Could you please provide the following information? It’s indeed expected for `qml.expval` to return a tensor in most cases, so my guess is that the problem is in how you’ve defined your cost function. However this additional information can help us know what’s going on.

1. The output of `qml.about()`

2. A minimal (but self-contained) working example  
This is the simplest version of the code that reproduces the problem. It should include all necessary imports, data, functions, etc., so that we can copy-paste the code and reproduce the problem. However it shouldn’t contain any unnecessary data, functions, …, for example gates and functions that can be removed to simplify the code.

3. The full error traceback (or the result you get in case there’s no error).

If you’re not sure what these mean then make sure to check out [this video](https://www.youtube.com/watch?v=snmOfreSPeY) 😃

---

<div class="post-metadata">

**Author:** ![phani.bharath](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/phani.bharath/32/2713_2.png) [@phani.bharath](https://discuss.pennylane.ai/u/phani.bharath)\
**Post date:** [July 17, 2024, 3:48am UTC](https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885/3 "2024-07-17T03:48:44Z")

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Hi Catalina, Thanks for your reply. Here is the info

1. qml.about()  
Name: PennyLane  
Version: 0.29.1  
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.  
Home-page: [GitHub - PennyLaneAI/pennylane: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.](https://github.com/XanaduAI/pennylane)  
Author:  
Author-email:  
License: Apache License 2.0  
Location: /opt/anaconda3/lib/python3.11/site-packages  
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, retworkx, scipy, semantic-version, toml  
Required-by: PennyLane-Lightning, PennyLane-SF

1. I am trying to solve keeping\_expectations\_low challenge. I implemented optimize\_circuit like this. variational\_circuit is returing expval as tensor object.

```auto
import json
import pennylane as qml
import pennylane.numpy as np

WIRES = 2
LAYERS = 5
NUM_PARAMETERS = LAYERS * WIRES * 3

initial_params = np.random.random(NUM_PARAMETERS)

def variational_circuit(params,hamiltonian):
    
    parameters = params.reshape((LAYERS, WIRES, 3))
    qml.templates.StronglyEntanglingLayers(parameters, wires=range(WIRES))
    return qml.expval(qml.Hermitian(hamiltonian, wires = [0,1]))

def optimize_circuit(params,hamiltonian):
    max_iterations = 500
    tol_ener_diff = 1e-06
    step_size = 0.01
    for n in range(max_iterations):
        print(variational_circuit(params, hamiltonian))
        opt = qml.GradientDescentOptimizer(stepsize=step_size)
        params, prev_energy = opt.step_and_cost(variational_circuit, params, hamiltonian)
        energy = variational_circuit(params)
        # Calculate difference between new and old energies
        ener_diff = np.abs(energy - prev_energy)
    
        if n % 20 == 0:
            print(
                "Iteration = {:}, Energy = {:.8f} Ha, Convergence parameter = {"
                ":.8f} Ha".format(n, energy, conv)
            )
    
        if ener_diff <= tol_ener_diff:
            break
    return ener    

dev = qml.device('default.qubit', wires=2)
circuit = qml.QNode(variational_circuit, dev, interface="autograd")

def run(test_case_input: str) -> str:
       
    ins = np.array(json.loads(test_case_input), requires_grad = False)
    hamiltonian = np.array(ins,float).reshape((2 **WIRES), (2** WIRES))
    np.random.seed(1967)
    initial_params = np.random.random(NUM_PARAMETERS)
    out = str(optimize_circuit(initial_params,hamiltonian))    
    
    return out

def check(solution_output: str, expected_output: str) -> None:
    solution_output = json.loads(solution_output)
    expected_output = json.loads(expected_output)
    assert np.isclose(solution_output, expected_output, rtol=5e-2)

# These are the public test cases
test_cases = [
    ('[0.863327072347624,0.0167108057202516,0.07991447085492759,0.0854049026262154,0.0167108057202516,0.8237963773906136,-0.07695947154193797,0.03131548733285282,0.07991447085492759,-0.07695947154193795,0.8355417021014687,-0.11345916130631205,0.08540490262621539,0.03131548733285283,-0.11345916130631205,0.758156886827099]', '0.61745341'),
    ('[0.32158897156285354,-0.20689268438270836,0.12366748295758379,-0.11737425017261123,-0.20689268438270836,0.7747346055276305,-0.05159966365446514,0.08215539696259792,0.12366748295758379,-0.05159966365446514,0.5769050487087416,0.3853362904758938,-0.11737425017261123,0.08215539696259792,0.3853362904758938,0.3986256655167206]', '0.00246488')
]
# This will run the public test cases locally
for i, (input_, expected_output) in enumerate(test_cases):
    print(f"Running test case {i} with input '{input_}'...")
    output = run(input_)
    try:
        output = run(input_)

    except Exception as exc:
        print(f"Runtime Error. {exc}")

    else:
        if message := check(output, expected_output):
            print(f"Wrong Answer. Have: '{output}'. Want: '{expected_output}'.")

        else:
            print("Correct!")

```

when I am trying to run test cases mentioned in usecases I am getting below error.

1. complete error stacktrace

* * *

KeyError Traceback (most recent call last)  
File /opt/anaconda3/lib/python3.11/site-packages/autograd/core.py:233, in vspace(value)  
232 try:  
 → 233 return VSpace.mappingstype(value)  
234 except KeyError:

KeyError: \<class ‘pennylane.measurements.expval.ExpectationMP’\>

During handling of the above exception, another exception occurred:

TypeError Traceback (most recent call last)  
Cell In[61], line 26  
24 for i, (input\_, expected\_output) in enumerate(test\_cases):  
25 print(f"Running test case {i} with input ‘{input\_}’…")  
—\> 26 output = run(input\_)  
27 try:  
28 output = run(input\_)

Cell In[61], line 8, in run(test\_case\_input)  
6 np.random.seed(1967)  
7 initial\_params = np.random.random(NUM\_PARAMETERS)  
----\> 8 out = str(optimize\_circuit(initial\_params,hamiltonian))  
10 return out

Cell In[59], line 8, in optimize\_circuit(params, hamiltonian)  
6 print(variational\_circuit(params, hamiltonian))  
7 opt = qml.GradientDescentOptimizer(stepsize=step\_size)  
----\> 8 params, prev\_energy = opt.step\_and\_cost(variational\_circuit, params, hamiltonian)  
9 energy = variational\_circuit(params)  
10 # Calculate difference between new and old energies

File /opt/anaconda3/lib/python3.11/site-packages/pennylane/optimize/gradient\_descent.py:59, in GradientDescentOptimizer.step\_and\_cost(self, objective\_fn, grad\_fn, \*args, \*\*kwargs)  
39 def step\_and\_cost(self, objective\_fn, \*args, grad\_fn=None, \*\*kwargs):  
40 “”“Update trainable arguments with one step of the optimizer and return the corresponding  
41 objective function value prior to the step.  
42  
(…)  
56 If single arg is provided, list [array] is replaced by array.  
57 “””  
—\> 59 g, forward = self.compute\_grad(objective\_fn, args, kwargs, grad\_fn=grad\_fn)  
60 new\_args = self.apply\_grad(g, args)  
62 if forward is None:

File /opt/anaconda3/lib/python3.11/site-packages/pennylane/optimize/gradient\_descent.py:117, in GradientDescentOptimizer.compute\_grad(objective\_fn, args, kwargs, grad\_fn)  
99 r""“Compute gradient of the objective function at the given point and return it along with  
100 the objective function forward pass (if available).  
101  
(…)  
114 will not be evaluted and instead `None` will be returned.  
115 “””  
116 g = get\_gradient(objective\_fn) if grad\_fn is None else grad\_fn  
 → 117 grad = g(\*args, \*\*kwargs)  
118 forward = getattr(g, “forward”, None)  
120 num\_trainable\_args = sum(getattr(arg, “requires\_grad”, False) for arg in args)

File /opt/anaconda3/lib/python3.11/site-packages/pennylane/\_grad.py:115, in grad. **call** (self, \*args, \*\*kwargs)  
112 self.\_forward = self.\_fun(\*args, \*\*kwargs)  
113 return ()  
 → 115 grad\_value, ans = grad\_fn(\*args, \*\*kwargs)  
116 self.\_forward = ans  
118 return grad\_value

File /opt/anaconda3/lib/python3.11/site-packages/autograd/wrap\_util.py:20, in unary\_to\_nary..nary\_operator..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)

File /opt/anaconda3/lib/python3.11/site-packages/pennylane/\_grad.py:135, in grad.\_grad\_with\_forward(fun, x)  
130 “”“This function is a replica of `autograd.grad`, with the only  
131 difference being that it returns both the gradient _and_ the forward pass  
132 value.”“”  
133 vjp, ans = \_make\_vjp(fun, x)  
 → 135 if not vspace(ans).size == 1:  
136 raise TypeError(  
137 "Grad only applies to real scalar-output functions. "  
138 “Try jacobian, elementwise\_grad or holomorphic\_grad.”  
139 )  
141 grad\_value = vjp(vspace(ans).ones())

File /opt/anaconda3/lib/python3.11/site-packages/autograd/core.py:238, in vspace(value)  
236 return vspace(getval(value))  
237 else:  
 → 238 raise TypeError("Can’t find vector space for value {} of type {}. "  
239 “Valid types are {}”.format(  
240 value, type(value), VSpace.mappings.keys()))

TypeError: Can’t find vector space for value expval(Hermitian(tensor([[0.86332707, 0.01671081, 0.07991447, 0.0854049],  
[0.01671081, 0.82379638, -0.07695947, 0.03131549],  
[0.07991447, -0.07695947, 0.8355417 , -0.11345916],  
[0.0854049 , 0.03131549, -0.11345916, 0.75815689]], requires\_grad=False), wires=[0, 1])) of type \<class ‘pennylane.measurements.expval.ExpectationMP’\>. Valid types are dict\_keys([\<class ‘autograd.core.SparseObject’\>, \<class ‘list’\>, \<class ‘tuple’\>, \<class ‘dict’\>, \<class ‘numpy.ndarray’\>, \<class ‘float’\>, \<class ‘numpy.float128’\>, \<class ‘numpy.float64’\>, \<class ‘numpy.float32’\>, \<class ‘numpy.float16’\>, \<class ‘complex’\>, \<class ‘numpy.complex256’\>, \<class ‘numpy.complex64’\>, \<class ‘numpy.complex128’\>, \<class ‘pennylane.numpy.tensor.tensor’\>])

---

<div class="post-metadata">

**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [July 18, 2024, 2:32am UTC](https://discuss.pennylane.ai/t/qml-expval-is-returning-tensor-object/4885/4 "2024-07-18T02:32:20Z")

</div>

Hi @phani.bharath ,

You’re using a very old version of PennyLane. Please make sure to update it with `pip install pennylane --upgrade`

Also, for future questions about this challenge it’s better to ask them on [this thread](https://discuss.pennylane.ai/t/pennylane-challenge-keeping-expectations-low/4501/5) so that others can find answers there too!

I hope this solves your issue.