Update app.py
Browse files
app.py
CHANGED
@@ -127,9 +127,7 @@ problem = QuadraticProgram("my_qubo")
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problem.binary_var("x0")
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problem.binary_var("x1")
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# Objective: minimize H = x0 + 2*x1 +
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# - linear coefficients => x0: 1, x1: 2
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# - quadratic coefficient => (x0, x1): 1
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problem.minimize(
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linear={"x0": 1, "x1": 2},
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quadratic={("x0", "x1"): 1}
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@@ -139,10 +137,8 @@ problem.minimize(
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print("\n--- Quadratic Program ---")
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print(problem.export_as_lp_string())
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# 2) Set up
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backend = Aer.get_backend('qasm_simulator')
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# Configure the quantum instance
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quantum_instance = QuantumInstance(
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backend=backend,
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shots=512,
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@@ -150,24 +146,22 @@ quantum_instance = QuantumInstance(
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seed_transpiler=42
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)
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# Create a QAOA instance
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qaoa = QAOA(
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optimizer=SPSA(maxiter=50),
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reps=2,
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quantum_instance=quantum_instance
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)
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# Wrap QAOA in a MinimumEigenOptimizer
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solver = MinimumEigenOptimizer(qaoa)
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# 3) Solve the QUBO
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result = solver.solve(problem)
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# Print results
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print("\n--- QAOA Results ---")
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print("Optimal solution:", result.x)
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print("Objective value:", result.fval)
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""",
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"9. DNA Sequence Matching with Grover's Algorithm": """
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from qiskit import QuantumCircuit, transpile
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problem.binary_var("x0")
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problem.binary_var("x1")
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# Objective: minimize: H = x0 + 2*x1 + x0*x1
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problem.minimize(
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linear={"x0": 1, "x1": 2},
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quadratic={("x0", "x1"): 1}
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print("\n--- Quadratic Program ---")
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print(problem.export_as_lp_string())
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# 2) Set up QAOA solver on the qasm_simulator
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backend = Aer.get_backend('qasm_simulator')
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quantum_instance = QuantumInstance(
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backend=backend,
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shots=512,
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seed_transpiler=42
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)
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qaoa = QAOA(
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optimizer=SPSA(maxiter=50),
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reps=2, # number of QAOA layers
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quantum_instance=quantum_instance
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)
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solver = MinimumEigenOptimizer(qaoa)
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# 3) Solve the QUBO
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result = solver.solve(problem)
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print("\n--- QAOA Results ---")
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print("Optimal solution:", result.x)
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print("Objective value:", result.fval)
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""",
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"9. DNA Sequence Matching with Grover's Algorithm": """
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from qiskit import QuantumCircuit, transpile
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