Quantum Machine Learning Analysis

Click any section to navigate:

Section Title Description
1.1 Quantum System Parameters Hamiltonian and Control specifications
1.2 Dataset Categories 1-qubit and 2-qubit configurations
1.3 Control Pulse Sequences Gaussian and Square pulses
1.4 Noise Profiles (N0-N6) Quantum noise characterization
1.5 State Evolution Bloch sphere trajectories
1.6 Pauli Measurements Expectation values over time
1.7 V₀ Operator Analysis Noise characterization
1.8 Deep Learning Model Quantum control optimization
1.9 Training Performance Loss and accuracy curves
1.10 Quantum Tomography State reconstruction
1.11 Noise Spectroscopy PSD analysis
1.12 Benchmark Results Algorithm comparison

Table 4: Quantum Control Framework

Feature Symbol Value/Range Description
Total Time T 1.0 Duration of simulation
Time Steps M 1024 Number of discrete steps
Noise Realizations K 2000 Number of noise instances
Energy Gap (1-qubit) Ω 12 Energy splitting of a single qubit
Pulses per Control n 5 Number of control pulses
Amplitude Range A_min, A_max [-100, 100] Range of pulse amplitudes