Small fixes to some demos during feature freeze for PL 0.39

demonstrations/tutorial_fermionic_operators.metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-06-27T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-30T00:00:00+00:00",
     "categories": [
         "Quantum Chemistry"
     ],

demonstrations/tutorial_fermionic_operators.py

@@ -40,15 +40,15 @@
 
 fermi_word = a0_dag * a1
 fermi_sentence = 1.3 * a0_dag * a1 + 2.4 * a1
-fermi_sentence
+print(fermi_sentence)
 
 ##############################################################################
 # In this simple example, we first created the operator :math:`a^{\dagger}_0 a_1` and then created
 # the linear combination :math:`1.3 a^{\dagger}_0 a_1 + 2.4 a_1.` We can also perform
 # arithmetic operations between Fermi words and Fermi sentences.
 
 fermi_sentence = fermi_sentence * fermi_word + 2.3 * fermi_word
-fermi_sentence
+print(fermi_sentence)
 
 ##############################################################################
 # Beyond multiplication, summation, and subtraction, we can exponentiate fermionic operators in
@@ -61,7 +61,7 @@
 # in the same way that you would write down the operator on a piece of paper:
 
 fermi_sentence = 1.2 * a0_dag + 0.5 * a1 - 2.3 * (a0_dag * a1) ** 2
-fermi_sentence
+print(fermi_sentence)
 
 ##############################################################################
 # This Fermi sentence can be mapped to the qubit basis using the

demonstrations/tutorial_optimal_control.metadata.json

@@ -6,7 +6,7 @@
         }
     ],
     "dateOfPublication": "2023-08-08T00:00:00+00:00",
-    "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+    "dateOfLastModification": "2024-10-30T00:00:00+00:00",
     "categories": [
         "Optimization",
         "Quantum Computing",

demonstrations/tutorial_optimal_control.py

@@ -504,7 +504,7 @@ def profit(params):
 # Initial parameters for the start and end times of the rectangles
 times = [jnp.linspace(eps, T - eps, P * 2) for op in ops_param]
 # All initial parameters: small alternating amplitudes and times
-params = [jnp.hstack([[0.1 * (-1) ** i for i in range(P)], time]) for time in times]
+params = [jnp.hstack([jnp.array([0.1 * (-1) ** i for i in range(P)]), time]) for time in times]
 
 #############################################################################
 # Now we are all set up to train the parameters of the pulse sequence to produce
@@ -679,7 +679,7 @@ def profit(params):
 # produced plot.
 
 times = [jnp.linspace(eps, T - eps, P * 2) for op in ops_param]
-params = [jnp.hstack([[0.2 * (-1) ** i for i in range(P)], time]) for time in times]
+params = [jnp.hstack([jnp.array([0.2 * (-1) ** i for i in range(P)]), time]) for time in times]
 
 num_steps = 1200
 learning_rate = -2e-3