style: format code with Autopep8, Black, ClangFormat, dotnet-format, …

…Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS, StandardRB, swift-format and Yapf

This commit fixes the style issues introduced in 22383ad according to the output
from Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java
Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt,
Scalafmt, StandardJS, StandardRB, swift-format and Yapf.

Details: None

quantum_module/quantum_optimization.py

@@ -1,42 +1,51 @@
 # Import necessary libraries
 import numpy as np
 from qiskit import QuantumCircuit, execute
-from qiskit.providers.aer import AerSimulator
 from qiskit.optimization import QuadraticProgram
+from qiskit.providers.aer import AerSimulator
 
 # Define a function to optimize a portfolio using quantum computing
+
+
 def optimize_portfolio(stocks, weights, risk_tolerance):
     # Define the quadratic program
     qp = QuadraticProgram()
-    qp.binary_var_list(stocks, name='x')
+    qp.binary_var_list(stocks, name="x")
     qp.minimize(linear={stocks[i]: weights[i] for i in range(len(stocks))})
-    qp.subject_to(LinearConstraint(sense='LE', rhs=risk_tolerance, coeffs={stocks[i]: weights[i] for i in range(len(stocks))}))
-
+    qp.subject_to(
+        LinearConstraint(
+            sense="LE",
+            rhs=risk_tolerance,
+            coeffs={stocks[i]: weights[i] for i in range(len(stocks))},
+        )
+    )
+
     # Convert the quadratic program to a QUBO problem
     qubo = qp.to_qubo()
-    
+
     # Create a quantum circuit to solve the QUBO problem
     qc = QuantumCircuit(len(stocks))
     qc.h(range(len(stocks)))
     qc.barrier()
     qc.x(range(len(stocks)))
     qc.barrier()
     qc.measure_all()
-    
+
     # Execute the circuit on a simulator
     simulator = AerSimulator()
     job = execute(qc, simulator, shots=1000)
     result = job.result()
     counts = result.get_counts(qc)
-    
+
     # Extract the optimal solution from the counts
     optimal_solution = max(counts, key=counts.get)
-    optimal_portfolio = [stocks[i] for i, x in enumerate(optimal_solution) if x == '1']
-    
+    optimal_portfolio = [stocks[i] for i, x in enumerate(optimal_solution) if x == "1"]
+
     return optimal_portfolio
 
+
 # Example usage
-stocks = ['AAPL', 'GOOG', 'MSFT']
+stocks = ["AAPL", "GOOG", "MSFT"]
 weights = [0.3, 0.4, 0.3]
 risk_tolerance = 0.5
 optimal_portfolio = optimize_portfolio(stocks, weights, risk_tolerance)