Addressing Ruslan's pull request comments.

jpmorganchase · Jan 9, 2025 · a754a40 · a754a40
1 parent fd3e946
commit a754a40
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Showing 4 changed files with 1 addition and 5 deletions.
diff --git a/qokit/dicke_state_utils.py b/qokit/dicke_state_utils.py
@@ -7,7 +7,6 @@
 
 import math, numpy, scipy
 
-# from qiskit.opflow import I, X, Y
 from qiskit.quantum_info import Pauli
 
 I = Pauli("I")

diff --git a/qokit/portfolio_optimization.py b/qokit/portfolio_optimization.py
@@ -84,14 +84,13 @@ def portfolio_brute_force(po_problem: dict, return_bitstring=False) -> tuple[flo
         return min_constrained, min_x, max_constrained, max_x, mean_constrained
 
 
-def get_data(N, seed=1, real=True) -> tuple[float, float]:
+def get_data(N, seed=1, real=False) -> tuple[float, float]:
     """
     load portofolio data from qiskit-finance (Yahoo)
     https://github.com/Qiskit/qiskit-finance/blob/main/docs/tutorials/11_time_series.ipynb
     """
     import datetime
 
-    # from qiskit_finance.data_providers import RandomDataProvider, YahooDataProvider
     from qokit.yahoo import YahooDataProvider
 
     stock_symbols = [

diff --git a/qokit/qaoa_circuit_portfolio.py b/qokit/qaoa_circuit_portfolio.py
@@ -209,7 +209,6 @@ def measure_circuit(circuit, n_trials=1024, save_state=True):
         return bitstrings
     else:
         backend = Aer.get_backend("statevector_simulator")
-        # result = transpile(circuit, backend).result()
         circ = transpile(circuit, backend)
         state = Statevector(circ)
         return reverse_array_index_bit_order(state)

diff --git a/qokit/qaoa_objective.py b/qokit/qaoa_objective.py
@@ -78,7 +78,6 @@ def compute_objective_from_probabilities(probabilities):  # type: ignore
 
         def g(gamma, beta):
             qc = parameterized_circuit.assign_parameters(list(np.hstack([beta, gamma])))
-            # qc = parameterized_circuit.bind_parameters(list(np.hstack([beta, gamma])))
             sv = np.asarray(backend.run(qc).result().get_statevector())
             probs = np.abs(sv) ** 2
             return compute_objective_from_probabilities(probs)