AIXARIA
VQE Optimization Analyzer
Interactive Variational Quantum Eigensolver Analysis Tool
Use Case
Quantum Chemistry - Hydrogen (H₂)
QAOA - Graph MaxCut
NISQ - Ising Model
QML - Binary Classifier
Finance - Portfolio Optimization
Adaptive VQE - Auto-Grow
Quantum Gem - Hybrid Prompt Defense
Optimizer
Adam
COBYLA
Gradient Descent
Learning Rate:
0.10
Max Iterations:
100
Play
Pause
Reset
Optimization Dashboard
Loss Landscape
Circuit Statistics
Parameter Evolution
Performance Metrics
Optimization Statistics
Current Cost
0.000
Best Cost
0.000
Iteration
0
Convergence Rate
0%
Current Problem
Molecular H₂ ground state
Qubits:
4
Layers:
3
Parameters:
12
Cost Function Convergence
Loss Landscape - 2D Parameter Space
Parameter Slice: θ
0
vs θ
1
Good regions (low cost)
Barren plateaus (high cost)
Optimization path
Circuit Configuration
Qubits:
4
Layers:
3
Gate Count Breakdown
Circuit Metrics
Total Gates
28
Circuit Depth
12
Parameter Count
12
Est. Execution Time
2.4 ms
Memory Required
64 KB
Parameter Distribution
Parameter Radar View
Parameter Sensitivity Heatmap
Individual Parameter History
Optimizer Comparison
Optimizer
Iterations to Converge
Final Error
Stability Score
Success Rate
Adam
87
0.012
0.91
94%
COBYLA
124
0.018
0.87
88%
Gradient Descent
96
0.015
0.85
92%
Noise Impact Simulation
Key Performance Indicators
95%
Convergence Rate
Percentage of runs reaching target accuracy
0.87
Stability Score
Consistency across different initializations
0.12
Noise Sensitivity
Lower is better - robustness to measurement noise
92%
Success Rate
Trials reaching global minimum