Update app.py

app.py

@@ -158,56 +158,6 @@ result = simulator.run(compiled_circuit, shots=1024).result()
 # Retrieve and print the counts
 counts = result.get_counts()
 print(counts)
-""",
-    "9. Genetic Variant Interaction": """
-from qiskit import QuantumCircuit, Aer
-from qiskit_aer import AerSimulator
-
-# Genetic variants as entangled qubits
-qc = QuantumCircuit(2, 2)
-qc.h(0)  # Variant 1 in superposition
-qc.cx(0, 1)  # Entangle with Variant 2
-qc.measure([0, 1], [0, 1])
-
-simulator = AerSimulator()
-compiled_circuit = transpile(qc, simulator)
-result = simulator.run(compiled_circuit, shots=1024).result()
-counts = result.get_counts()
-print(counts)
-""",
-    "10. DNA Alignment Scoring": """
-from qiskit import QuantumCircuit, Aer
-from qiskit_aer import AerSimulator
-
-# Create a Quantum Circuit for DNA alignment
-qc = QuantumCircuit(3, 3)
-
-# Simulate possible alignments with superposition
-qc.h([0, 1, 2])  # 3 alignments in parallel
-qc.measure([0, 1, 2], [0, 1, 2])
-
-simulator = AerSimulator()
-compiled_circuit = transpile(qc, simulator)
-result = simulator.run(compiled_circuit, shots=1024).result()
-counts = result.get_counts()
-print(counts)
-""",
-    "11. Protein Folding Simulation with QAOA": """
-from qiskit import Aer, QuantumCircuit
-from qiskit.algorithms.optimizers import COBYLA
-from qiskit_aer import AerSimulator
-from qiskit.algorithms.minimum_eigensolvers import QAOA
-from qiskit.circuit.library import TwoLocal
-
-# Build a QAOA circuit for protein folding
-p = 1
-ansatz = TwoLocal(2, "ry", "cz", reps=p)
-qaoa = QAOA(ansatz=ansatz, optimizer=COBYLA())
-
-# Simulate energy minimization
-simulator = AerSimulator()
-qaoa_result = qaoa.compute_minimum_eigenvalue(operator=None)
-print(qaoa_result)
 """
 }