Fix linting issues: reduce errors from 1363 to 87

- Fixed import issues, unused variables, and formatting
- Removed deprecated typing imports
- Fixed whitespace and trailing spaces
- Tests pass successfully (21/21 measurements tests)
- Package imports correctly

Author: Shashikant86

examples/quantum_ml.py

@@ -588,12 +588,12 @@ def main():
     datasets = generate_quantum_datasets()
 
     # Demonstrate feature maps
-    feature_maps = demonstrate_feature_maps()
+    demonstrate_feature_maps()
 
     # Run examples
     classification_results = quantum_classification_example(datasets, client)
     regression_results = quantum_regression_example(datasets, client)
-    network_results = quantum_neural_network_example(datasets, client)
+    quantum_neural_network_example(datasets, client)
 
     # Visualize results
     visualize_results(classification_results, regression_results)

examples/vqe_molecule.py

@@ -19,7 +19,7 @@
 def hydrogen_molecule_hamiltonian():
     """
     Create Hamiltonian for H2 molecule in STO-3G basis
-    
+
     This is a simplified 2-qubit Hamiltonian for H2 at equilibrium
     bond distance (0.74 Å) using Jordan-Wigner mapping.
     """
@@ -49,11 +49,11 @@ def hydrogen_molecule_hamiltonian():
 def create_hardware_efficient_ansatz(num_qubits, num_layers):
     """
     Create hardware-efficient ansatz for VQE
-    
+
     Args:
         num_qubits: Number of qubits
         num_layers: Number of ansatz layers
-    
+
     Returns:
         Function that creates ansatz circuit from parameters
     """
@@ -89,7 +89,7 @@ def ansatz(parameters):
 def create_uccsd_ansatz(num_qubits):
     """
     Create simplified UCCSD (Unitary Coupled Cluster) ansatz
-    
+
     This is a simplified version focusing on single and double excitations
     for educational purposes.
     """
@@ -127,13 +127,13 @@ def ansatz(parameters):
 def run_vqe_simulation(hamiltonian, ansatz, num_params, client=None):
     """
     Run VQE optimization using simulation
-    
+
     Args:
         hamiltonian: Target Hamiltonian
         ansatz: Parameterized ansatz function
         num_params: Number of parameters
         client: SuperQuantX client (None for local simulation)
-    
+
     Returns:
         VQE results dictionary
     """
@@ -376,7 +376,7 @@ def main():
     # Example 4: Advanced features
     print("\n\n4. Advanced VQE Features")
     print("-" * 25)
-    feature_results = demonstrate_vqe_features()
+    demonstrate_vqe_features()
 
     # Example 5: Visualization (if matplotlib available)
     try:

src/superquantx/__init__.py

@@ -1,35 +1,35 @@
 """SuperQuantX: Experimental Quantum AI Research Platform.
 
-⚠️ RESEARCH SOFTWARE WARNING: This is experimental research software developed by 
-SuperXLab (Superagentic AI Research Division). NOT intended for production use. 
+⚠️ RESEARCH SOFTWARE WARNING: This is experimental research software developed by
+SuperXLab (Superagentic AI Research Division). NOT intended for production use.
 For research and educational purposes only.
 
-Part of SuperXLab's comprehensive quantum research program - the practical implementation 
+Part of SuperXLab's comprehensive quantum research program - the practical implementation
 platform for validating theoretical research in:
     🔬 Quantum-Inspired Agentic Systems: Superposition, interference, entanglement in agents
-    🔬 Quantum Neural Networks (QNNs): Hardware-validated quantum neural architectures  
+    🔬 Quantum Neural Networks (QNNs): Hardware-validated quantum neural architectures
     🔬 QuantumML for AI Training: Quantum-accelerated machine learning techniques
     🔬 Quantinuum Integration: Real hardware validation on H-Series quantum computers
 
 Research Examples:
     Experimental quantum agent research:
         >>> import superquantx as sqx  # EXPERIMENTAL
-        >>> agent = sqx.QuantumTradingAgent(strategy="quantum_portfolio", backend="simulator") 
+        >>> agent = sqx.QuantumTradingAgent(strategy="quantum_portfolio", backend="simulator")
         >>> results = agent.solve(research_data)  # Research use only
         >>> print(f"Research findings: {results.metadata}")
-        
+
     Quantum neural network experiments:
         >>> qnn = sqx.QuantumNN(architecture='hybrid', backend='pennylane')  # EXPERIMENTAL
         >>> qnn.fit(X_research, y_research)  # Research data only
         >>> analysis = qnn.analyze_expressivity()  # Research analysis
-        
+
     Quantum algorithm benchmarking:
-        >>> qsvm = sqx.QuantumSVM(backend='simulator')  # EXPERIMENTAL  
+        >>> qsvm = sqx.QuantumSVM(backend='simulator')  # EXPERIMENTAL
         >>> benchmark = sqx.benchmark_algorithm(qsvm, classical_baseline)
 """
 
 import logging
-from typing import Any, Dict
+from typing import Any
 
 # Core imports - make available at top level
 from . import algorithms, backends, cli, datasets, utils
@@ -172,7 +172,7 @@ def get_version() -> str:
     """Get SuperQuantX version."""
     return __version__
 
-def get_backend_info() -> Dict[str, Any]:
+def get_backend_info() -> dict[str, Any]:
     """Get information about available backends."""
     info = {}