updates

Unibw 2023/misc/quantum/test.py

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+from qiskit import QuantumCircuit, Aer, transpile, assemble
+from qiskit.aqua.components.optimizers import COBYLA
+from qiskit.circuit import Parameter
+import numpy as np
+
+# Define the desired probability distribution
+target_probs = [0.1666666666] * 6
+
+# Define the quantum circuit
+circ = QuantumCircuit(6, 6)
+circ.h(range(6))
+
+# Add the parameterized Ansatz using Parameter
+params = [Parameter(f'var{i}') for i in range(6)]
+circ.rx(params[0], 0)
+circ.rx(params[1], 1)
+circ.rx(params[2], 2)
+circ.rx(params[3], 3)
+circ.rx(params[4], 4)
+circ.rx(params[5], 5)
+
+
+# Define the optimization objective function
+def objective_function(params):
+    # Update the circuit with new parameters
+    updated_circ = circ.bind_parameters({params[i]: optimal_params[i] for i in range(6)})
+
+    # Compile and run the circuit
+    t_circ = transpile(updated_circ, Aer.get_backend('qasm_simulator'))
+    qobj = assemble(t_circ)
+    result = Aer.get_backend('qasm_simulator').run(qobj).result()
+
+    # Calculate the probability distribution
+    counts = result.get_counts()
+    probs = [counts.get(format(i, '06b'), 0) / result.shots for i in range(2 ** 6)]
+
+    # Calculate the objective function (sum of squared differences)
+    return sum((probs[i] - target_probs[i]) ** 2 for i in range(2 ** 6))
+
+
+# Optimize the circuit parameters
+optimizer = COBYLA(maxiter=1000)
+optimal_params = optimizer.optimize(num_vars=6, objective_function=objective_function)
+
+# Print the optimal parameters
+print("Optimal Parameters:", optimal_params)
+