Replace bind_parameters with assign_parameters (#79)

qiskit-community · Sep 13, 2023 · 0c7d25c · 0c7d25c
1 parent a387f3e
commit 0c7d25c
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Showing 8 changed files with 10 additions and 10 deletions.
diff --git a/docs/tutorials/10_pvqd.ipynb b/docs/tutorials/10_pvqd.ipynb
@@ -731,7 +731,7 @@
    "source": [
     "from qiskit.quantum_info import Statevector\n",
     "\n",
-    "initial_state = Statevector(ansatz.bind_parameters(initial_parameters))\n",
+    "initial_state = Statevector(ansatz.assign_parameters(initial_parameters))\n",
     "exact_times, exact_energies, exact_magnetizations = exact(\n",
     "    final_time, 0.01, hamiltonian, initial_state\n",
     ")"
@@ -1012,4 +1012,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
diff --git a/qiskit_algorithms/eigensolvers/vqd.py b/qiskit_algorithms/eigensolvers/vqd.py
@@ -270,7 +270,7 @@ def compute_eigenvalues(
                 initial_point = validate_initial_point(initial_points[step - 1], self.ansatz)
 
             if step > 1:
-                prev_states.append(self.ansatz.bind_parameters(result.optimal_points[-1]))
+                prev_states.append(self.ansatz.assign_parameters(result.optimal_points[-1]))
 
             self._eval_count = 0
             energy_evaluation = self._get_evaluate_energy(

diff --git a/qiskit_algorithms/gradients/reverse/bind.py b/qiskit_algorithms/gradients/reverse/bind.py
@@ -26,7 +26,7 @@ def bind(
     """Bind parameters in a circuit (or list of circuits).
 
     This method also allows passing parameter binds to parameters that are not in the circuit,
-    and thereby differs to :meth:`.QuantumCircuit.bind_parameters`.
+    and thereby differs to :meth:`.QuantumCircuit.assign_parameters`.
 
     Args:
         circuits: Input circuit(s).

diff --git a/qiskit_algorithms/optimizers/spsa.py b/qiskit_algorithms/optimizers/spsa.py
@@ -97,7 +97,7 @@ class SPSA(Optimizer):
             initial_point = np.random.random(ansatz.num_parameters)
 
             def loss(x):
-                bound = ansatz.bind_parameters(x)
+                bound = ansatz.assign_parameters(x)
                 return np.real((StateFn(observable, is_measurement=True) @ StateFn(bound)).eval())
 
             spsa = SPSA(maxiter=300)

diff --git a/qiskit_algorithms/time_evolvers/pvqd/pvqd.py b/qiskit_algorithms/time_evolvers/pvqd/pvqd.py
@@ -233,7 +233,7 @@ def get_loss(
         self._validate_setup(skip={"optimizer"})
 
         # use Trotterization to evolve the current state
-        trotterized = ansatz.bind_parameters(current_parameters)
+        trotterized = ansatz.assign_parameters(current_parameters)
 
         evolution_gate = PauliEvolutionGate(hamiltonian, time=dt, synthesis=self.evolution)
 
@@ -389,7 +389,7 @@ def evolve(self, evolution_problem: TimeEvolutionProblem) -> TimeEvolutionResult
             if observables is not None:
                 observable_values.append(evaluate_observables(next_parameters))
 
-        evolved_state = self.ansatz.bind_parameters(parameters[-1])
+        evolved_state = self.ansatz.assign_parameters(parameters[-1])
 
         result = PVQDResult(
             evolved_state=evolved_state,

diff --git a/test/optimizers/test_gradient_descent.py b/test/optimizers/test_gradient_descent.py
@@ -61,7 +61,7 @@ def test_pauli_two_design(self):
         )
 
         def objective_pauli(x):
-            bound_circ = circuit.bind_parameters(dict(zip(parameters, x)))
+            bound_circ = circuit.assign_parameters(dict(zip(parameters, x)))
             return Statevector(bound_circ).expectation_value(obs).real
 
         optimizer = GradientDescent(maxiter=100, learning_rate=0.1, perturbation=0.1)

diff --git a/test/optimizers/test_spsa.py b/test/optimizers/test_spsa.py
@@ -47,7 +47,7 @@ def test_pauli_two_design(self, method):
         )
 
         def objective(x):
-            bound_circ = circuit.bind_parameters(dict(zip(parameters, x)))
+            bound_circ = circuit.assign_parameters(dict(zip(parameters, x)))
             return Statevector(bound_circ).expectation_value(obs).real
 
         settings = {"maxiter": 100, "blocking": True, "allowed_increase": 0}

diff --git a/test/time_evolvers/variational/test_var_qrte.py b/test/time_evolvers/variational/test_var_qrte.py
@@ -66,7 +66,7 @@ def expected_state(time):
         result = varqrte.evolve(evolution_problem)
 
         final_parameters = result.parameter_values[-1]
-        final_state = Statevector(circuit.bind_parameters(final_parameters)).to_dict()
+        final_state = Statevector(circuit.assign_parameters(final_parameters)).to_dict()
         final_expected_state = expected_state(final_time)
 
         for key, expected_value in final_state.items():