Update app.py

app.py

@@ -96,7 +96,93 @@ result = simulator.run(compiled_circuit, shots=1024).result()
 counts = result.get_counts()
 print(counts)
 """,
-    # Add more examples here, progressively getting more complex
+    "7. DNA Base Pair Encoding": """
+from qiskit import QuantumCircuit
+qc = QuantumCircuit(2, 2)
+
+# DNA Base Pair Encoding: A -> 00, T -> 01, G -> 10, C -> 11
+qc.x(0)  # Example encoding for T (01)
+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)
+""",
+    "8. DNA Sequence Matching with Grover's Algorithm": """
+from qiskit import QuantumCircuit, Aer
+from qiskit_aer import AerSimulator
+from qiskit.circuit.library import GroverOperator
+from qiskit.algorithms import AmplificationProblem
+
+# Oracle marks the target sequence
+def oracle(circuit):
+    circuit.cz(0, 1)  # Mark sequence 01 as a solution
+
+qc = QuantumCircuit(2)
+oracle(qc)
+
+# Grover search for the sequence
+problem = AmplificationProblem(qc)
+grover_circuit = GroverOperator(problem)
+simulator = AerSimulator()
+
+compiled_circuit = transpile(grover_circuit, simulator)
+result = simulator.run(compiled_circuit, shots=1024).result()
+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)
+"""
 }
 
 # Selection menu for examples