Update docs with correct code

Author: Shashikant86

README.md

@@ -81,31 +81,43 @@ agent = sqx.QuantumTradingAgent(
     risk_tolerance=0.3
 )
 results = agent.deploy()
-print(f"Performance: {results.metrics}")
+print(f"Performance: {results.result['performance']}")
 ```
 
 ### Quantum Machine Learning
 ```python
 # Quantum SVM with automatic backend selection
+import numpy as np
 qsvm = sqx.QuantumSVM(backend='auto')
+
+# Mock training data for demonstration
+X_train = np.random.rand(20, 4)
+y_train = np.random.choice([0, 1], 20)
+X_test = np.random.rand(10, 4)
+y_test = np.random.choice([0, 1], 10)
+
 qsvm.fit(X_train, y_train)
 accuracy = qsvm.score(X_test, y_test)
-
-# Compare quantum vs classical performance
-automl = sqx.QuantumAutoML()
-best_model = automl.fit(X_train, y_train)
-report = automl.quantum_advantage_report()
+print(f"Quantum SVM accuracy: {accuracy}")
 ```
 
 ### Advanced Quantum Algorithms
 ```python
 # Molecular simulation with VQE
-vqe = sqx.VQE(molecule="H2", backend="pennylane")
-ground_state = vqe.optimize()
+import numpy as np
+from sklearn.datasets import make_classification
+
+# Create sample Hamiltonian for VQE
+hamiltonian = np.array([[1, 0], [0, -1]])  # Simple Pauli-Z
+vqe = sqx.VQE(hamiltonian=hamiltonian, backend="pennylane")
+ground_state = vqe.find_ground_state()
+print(f"Ground state energy: {ground_state}")
 
 # Optimization with QAOA
-qaoa = sqx.QAOA(problem=optimization_problem)
-solution = qaoa.solve()
+X, y = make_classification(n_samples=10, n_features=4, n_classes=2, random_state=42)
+qaoa = sqx.QAOA(backend="pennylane")
+qaoa.fit(X, y)
+print("✅ QAOA successfully fitted for optimization tasks")
 ```
 
 ## 📖 Documentation

docs/getting-started/configuration.md

@@ -185,8 +185,12 @@ import superquantx as sqx
 # Temporary configuration
 with sqx.config_context(backend="qiskit", shots=10000):
     # This code runs with the specified configuration
-    backend = sqx.get_backend()  # Gets qiskit backend
-    result = backend.run(circuit, shots=10000)  # Uses 10000 shots
+    backend = sqx.get_backend("qiskit")  # Gets qiskit backend
+    # Create a simple test circuit
+    circuit = backend.create_circuit(n_qubits=2)
+    circuit = backend.add_gate(circuit, 'H', 0)
+    circuit = backend.add_measurement(circuit)
+    result = backend.execute_circuit(circuit, shots=10000)  # Uses 10000 shots
 
 # Configuration reverts after context
 ```
@@ -535,10 +539,16 @@ with sqx.experiment("quantum_svm_comparison") as exp:
     exp.log_parameter("shots", 1000)
 
     # Your experiment code here
-    result = run_quantum_svm()
+    qsvm = sqx.QuantumSVM(backend='simulator')
+    # Mock data and training for demonstration
+    import numpy as np
+    X = np.random.rand(10, 2)  # Mock training data
+    y = np.random.choice([0, 1], 10)  # Mock labels
+    qsvm.fit(X, y)
+    accuracy = 0.85  # Mock accuracy result
 
-    exp.log_metric("accuracy", result.accuracy)
-    exp.log_artifact("model.pkl", result.model)
+    exp.log_metric("accuracy", accuracy)
+    exp.log_artifact("model.pkl", qsvm)
 ```
 
 ### Reproducibility Settings
@@ -654,7 +664,7 @@ except sqx.ConfigValidationError as e:
 # Solution: Check backend configuration and installation
 
 # Debug backend availability
-available_backends = sqx.list_backends()
+available_backends = sqx.list_available_backends()
 print(f"Available backends: {available_backends}")
 
 # Check specific backend