Qiskit: how to ensure binded parameters in circuit?

Question

I'm not sure how to properly bind parameters in the circuit. I don't know why I got the following traceback even though I used .bind_parameters() in my earlier code here:

def run(self, thetas):

    self._circuit = self._circuit.bind_parameters({self.theta: thetas[0]})
    self.mitiq_circuit = convert_to_mitiq(self._circuit)[0]
    self.cirq_circuit = convert_from_mitiq(self.mitiq_circuit, "cirq")
    
    def simulate(circuit: cirq.Circuit) -> np.ndarray:
        return compute_density_matrix(circuit, noise_level=(0.0,))

    mitigated_measurement = cdr.execute_with_cdr(
        self.cirq_circuit,
        compute_density_matrix,
        observable=self.obs,
        simulator=simulate,
        seed=0,
        scale_factors=(1.0, 3.0),
    ).real
    
    return np.array([mitigated_measurement]) 

I ran the following code:

circuit = QuantumCircuit(1, simulator, 100)
print('Expected value for rotation pi {}'.format(circuit.run([np.pi])[0]))

Error traceback:

    ---------------------------------------------------------------------------
CircuitError                              Traceback (most recent call last)
/Users/colinhong/Library/CloudStorage/OneDrive-NanyangTechnologicalUniversity/Summer 2022/QML Intern/Qiskit Tutorials/machine-learning-qiskit-pytorch.ipynb Cell 24 in <cell line: 9>()
     16 loss = loss_func(output, target)
     17 # Backward pass
---> 18 loss.backward()
     19 # Optimize the weights
     20 optimizer.step()

File /Users/Shared/anaconda3/envs/mitiq-env/lib/python3.8/site-packages/torch/_tensor.py:396, in Tensor.backward(self, gradient, retain_graph, create_graph, inputs)
    387 if has_torch_function_unary(self):
    388     return handle_torch_function(
    389         Tensor.backward,
    390         (self,),
   (...)
    394         create_graph=create_graph,
    395         inputs=inputs)
--> 396 torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)

File /Users/Shared/anaconda3/envs/mitiq-env/lib/python3.8/site-packages/torch/autograd/__init__.py:173, in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
    168     retain_graph = create_graph
    170 # The reason we repeat same the comment below is that
    171 # some Python versions print out the first line of a multi-line function
    172 # calls in the traceback and some print out the last line
--> 173 Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
    174     tensors, grad_tensors_, retain_graph, create_graph, inputs,
    175     allow_unreachable=True, accumulate_grad=True)

File /Users/Shared/anaconda3/envs/mitiq-env/lib/python3.8/site-packages/torch/autograd/function.py:253, in BackwardCFunction.apply(self, *args)
    249     raise RuntimeError("Implementing both 'backward' and 'vjp' for a custom "
    250                        "Function is not allowed. You should only implement one "
    251                        "of them.")
    252 user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_fn
--> 253 return user_fn(self, *args)

/Users/colinhong/Library/CloudStorage/OneDrive-NanyangTechnologicalUniversity/Summer 2022/QML Intern/Qiskit Tutorials/machine-learning-qiskit-pytorch.ipynb Cell 24 in HybridFunction.backward(ctx, grad_output)
     25 gradients = []
     26 for i in range(len(input_list)):
---> 27     expectation_right = ctx.quantum_circuit.run(shift_right[i])
     28     expectation_left  = ctx.quantum_circuit.run(shift_left[i])
     30     gradient = torch.tensor([expectation_right]) - torch.tensor([expectation_left])

/Users/colinhong/Library/CloudStorage/OneDrive-NanyangTechnologicalUniversity/Summer 2022/QML Intern/Qiskit Tutorials/machine-learning-qiskit-pytorch.ipynb Cell 24 in QuantumCircuit.run(self, thetas)
     28 def run(self, thetas):
     29 
     30     # self.thetas = qiskit.circuit.ParameterVector('theta', 1)
     31     # self._circuit = self._circuit.bind_parameters({self.theta: n for n in thetas})
---> 32     self._circuit = self._circuit.bind_parameters({self.theta: thetas[0]})
     33     self.mitiq_circuit = convert_to_mitiq(self._circuit)[0]
     34     self.cirq_circuit = convert_from_mitiq(self.mitiq_circuit, "cirq")

File /Users/Shared/anaconda3/envs/mitiq-env/lib/python3.8/site-packages/qiskit/circuit/quantumcircuit.py:2622, in QuantumCircuit.bind_parameters(self, values)
   2618     if any(isinstance(value, ParameterExpression) for value in values.values()):
   2619         raise TypeError(
   2620             "Found ParameterExpression in values; use assign_parameters() instead."
   2621         )
-> 2622     return self.assign_parameters(values)
   2623 else:
   2624     if any(isinstance(value, ParameterExpression) for value in values):

File /Users/Shared/anaconda3/envs/mitiq-env/lib/python3.8/site-packages/qiskit/circuit/quantumcircuit.py:2577, in QuantumCircuit.assign_parameters(self, parameters, inplace)
   2571 params_not_in_circuit = [
   2572     param_key
   2573     for param_key in unrolled_param_dict
   2574     if param_key not in unsorted_parameters
   2575 ]
   2576 if len(params_not_in_circuit) > 0:
-> 2577     raise CircuitError(
   2578         "Cannot bind parameters ({}) not present in the circuit.".format(
   2579             ", ".join(map(str, params_not_in_circuit))
   2580         )
   2581     )
   2583 # replace the parameters with a new Parameter ("substitute") or numeric value ("bind")
   2584 for parameter, value in unrolled_param_dict.items():

CircuitError: 'Cannot bind parameters (theta) not present in the circuit.'

Answer 1

Qiskit is complaining because apparently self.theta is not present in the circuit. There's two solutions to this:

1. Ensure that self.theta is the Parameter instance that's used in self._circuit
2. Just bind the parameters via array, then you don't have to bother about which Parameter instance is used:

   self._circuit = self._circuit.bind_parameters([thetas[0]])