VQE性能分析可视化仪表板¶

本仪表板提供VQE算法性能瓶颈的可视化分析，帮助快速识别关键优化点。

In [1]:

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.patches import Rectangle
import warnings
warnings.filterwarnings('ignore')

# 设置中文字体和样式
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 8)

import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns from matplotlib.patches import Rectangle import warnings warnings.filterwarnings('ignore') # 设置中文字体和样式 plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = (12, 8)

📊 性能瓶颈概览¶

In [5]:

# Performance data
performance_data = {
    'function_name': [
        'one_qubit_base', '__call__', 'calculate_expectation_state', 
        'expectation', '__matmul__', 'apply_gate', 'cast', 
        'execute_circuit', 'circuit_call', 'execute'
    ],
    'total_time(s)': [802.0, 381.1, 478.9, 1349.0, 1316.0, 945.8, 330.4, 800.6, 805.1, 805.17],
    'call_count': [605616, 81840, 2046, 2046, 2046, 605616, 689622, 2046, 2046, 2046],
    'pertime(s)': [0.001316, 0.003683, 0.00658, 0.002651, 0.0002519, 1.162e-05, 2.574e-06, 0.001898, 1.054e-05, 1.054e-05],
    'cumulative_time(s)': [0.7818, 3.459, 10.94, 13.59, 13.84, 15.99, 16.68, 17.58, 17.64, 17.662]
}

df = pd.DataFrame(performance_data)
df['function_in'] = ['cpu.py:93', 'terms.py:291', 'numpy.py:788', 'hamiltonians.py:103', 
                  'hamiltonians.py:692', 'cpu.py:162', 'numpy.py:76', 'numpy.py:413', 
                  'circuit.py:1099', 'circuit.py:1062']

# Create figure
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
fig.suptitle('VQE Performance Analysis', fontsize=16, fontweight='bold')

# 1. Total time ranking
top_functions = df.nlargest(8, 'total_time(s)')
bars1 = ax1.barh(top_functions['function_name'], top_functions['total_time(s)'], 
                  color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8', '#F7DC6F'])
ax1.set_title('Top 8 Functions by Total Time', fontsize=12, fontweight='bold')
ax1.set_xlabel('Total Time (s)')
for i, v in enumerate(top_functions['total_time(s)']):
    ax1.text(v + 10, i, f'{v:.1f}s', va='center')

# 2. Call count distribution
top_calls = df.nlargest(8, 'call_count')
bars2 = ax2.bar(range(len(top_calls)), top_calls['call_count'], 
                 color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6', '#1ABC9C', '#34495E', '#E67E22'])
ax2.set_title('Top 8 Functions by Call Count', fontsize=12, fontweight='bold')
ax2.set_xlabel('Function')
ax2.set_ylabel('Call Count')
ax2.set_xticks(range(len(top_calls)))
ax2.set_xticklabels([name[:15] + '...' if len(name) > 15 else name for name in top_calls['function_name']], 
                   rotation=45, ha='right')
ax2.set_yscale('log')
for i, v in enumerate(top_calls['call_count']):
    ax2.text(i, v*1.1, f'{v:,}', ha='center', va='bottom')

# 3. Cumulative time vs call count scatter plot
scatter = ax3.scatter(df['call_count'], df['cumulative_time(s)'], 
                     s=df['total_time(s)']*2, alpha=0.6, 
                     c=df['total_time(s)'], cmap='Reds', edgecolors='black')
ax3.set_xlabel('Call Count')
ax3.set_ylabel('Cumulative Time (s)')
ax3.set_title('Call Count vs Cumulative Time', fontweight='bold')
ax3.set_xscale('log')
ax3.set_yscale('log')

# Add labels
for i, row in df.iterrows():
    if row['total_time(s)'] > 500 or row['call_count'] > 100000:
        ax3.annotate(row['function_name'][:10], 
                    (row['call_count'], row['cumulative_time(s)']),
                    xytext=(5, 5), textcoords='offset points', fontsize=8)

plt.colorbar(scatter, ax=ax3, label='Total Time (s)')

# 4. Optimization Priority Matrix
optimization_data = [
    {'Project': 'one_qubit_base optimization', 'Impact': 9, 'Difficulty': 2, 'Benefit': '1-10%'},
    {'Project': 'Matrix caching mechanism', 'Impact': 8, 'Difficulty': 5, 'Benefit': '5-15%'},
    {'Project': 'Data type unification', 'Impact': 6, 'Difficulty': 2, 'Benefit': '3-8%'},
    {'Project': 'Vectorized computation', 'Impact': 8, 'Difficulty': 6, 'Benefit': '10-20%'},
    {'Project': 'GPU acceleration', 'Impact': 10, 'Difficulty': 9, 'Benefit': '30-50%'},
    {'Project': 'Sparse matrix optimization', 'Impact': 7, 'Difficulty': 7, 'Benefit': '8-15%'},
    {'Project': 'Parallel processing', 'Impact': 8, 'Difficulty': 8, 'Benefit': '15-25%'}
]

opt_df = pd.DataFrame(optimization_data)

# Draw priority matrix
for i, row in opt_df.iterrows():
    color = 'red' if row['Impact'] >= 8 and row['Difficulty'] <= 5 else 'orange' if row['Impact'] >= 7 else 'lightblue'
    ax4.scatter(row['Difficulty'], row['Impact'], s=200, alpha=0.7, c=color, edgecolors='black')
    ax4.annotate(row['Project'], (row['Difficulty'], row['Impact']), 
                xytext=(5, 5), textcoords='offset points', fontsize=9)

# Add quadrant labels
ax4.axhline(y=7, color='gray', linestyle='--', alpha=0.5)
ax4.axvline(x=5, color='gray', linestyle='--', alpha=0.5)
ax4.text(2.5, 9, 'Quick Wins', fontsize=10, fontweight='bold', ha='center')
ax4.text(7.5, 9, 'Core Optimizations', fontsize=10, fontweight='bold', ha='center')
ax4.text(2.5, 5, 'Maintenance', fontsize=10, fontweight='bold', ha='center')
ax4.text(7.5, 5, 'Long-term Planning', fontsize=10, fontweight='bold', ha='center')

ax4.set_xlabel('Implementation Difficulty')
ax4.set_ylabel('Expected Impact')
ax4.set_title('Optimization Priority Matrix', fontweight='bold')
ax4.set_xlim(0, 10)
ax4.set_ylim(0, 10)
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Performance data performance_data = { 'function_name': [ 'one_qubit_base', '__call__', 'calculate_expectation_state', 'expectation', '__matmul__', 'apply_gate', 'cast', 'execute_circuit', 'circuit_call', 'execute' ], 'total_time(s)': [802.0, 381.1, 478.9, 1349.0, 1316.0, 945.8, 330.4, 800.6, 805.1, 805.17], 'call_count': [605616, 81840, 2046, 2046, 2046, 605616, 689622, 2046, 2046, 2046], 'pertime(s)': [0.001316, 0.003683, 0.00658, 0.002651, 0.0002519, 1.162e-05, 2.574e-06, 0.001898, 1.054e-05, 1.054e-05], 'cumulative_time(s)': [0.7818, 3.459, 10.94, 13.59, 13.84, 15.99, 16.68, 17.58, 17.64, 17.662] } df = pd.DataFrame(performance_data) df['function_in'] = ['cpu.py:93', 'terms.py:291', 'numpy.py:788', 'hamiltonians.py:103', 'hamiltonians.py:692', 'cpu.py:162', 'numpy.py:76', 'numpy.py:413', 'circuit.py:1099', 'circuit.py:1062'] # Create figure fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12)) fig.suptitle('VQE Performance Analysis', fontsize=16, fontweight='bold') # 1. Total time ranking top_functions = df.nlargest(8, 'total_time(s)') bars1 = ax1.barh(top_functions['function_name'], top_functions['total_time(s)'], color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFEAA7', '#DDA0DD', '#98D8C8', '#F7DC6F']) ax1.set_title('Top 8 Functions by Total Time', fontsize=12, fontweight='bold') ax1.set_xlabel('Total Time (s)') for i, v in enumerate(top_functions['total_time(s)']): ax1.text(v + 10, i, f'{v:.1f}s', va='center') # 2. Call count distribution top_calls = df.nlargest(8, 'call_count') bars2 = ax2.bar(range(len(top_calls)), top_calls['call_count'], color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6', '#1ABC9C', '#34495E', '#E67E22']) ax2.set_title('Top 8 Functions by Call Count', fontsize=12, fontweight='bold') ax2.set_xlabel('Function') ax2.set_ylabel('Call Count') ax2.set_xticks(range(len(top_calls))) ax2.set_xticklabels([name[:15] + '...' if len(name) > 15 else name for name in top_calls['function_name']], rotation=45, ha='right') ax2.set_yscale('log') for i, v in enumerate(top_calls['call_count']): ax2.text(i, v*1.1, f'{v:,}', ha='center', va='bottom') # 3. Cumulative time vs call count scatter plot scatter = ax3.scatter(df['call_count'], df['cumulative_time(s)'], s=df['total_time(s)']*2, alpha=0.6, c=df['total_time(s)'], cmap='Reds', edgecolors='black') ax3.set_xlabel('Call Count') ax3.set_ylabel('Cumulative Time (s)') ax3.set_title('Call Count vs Cumulative Time', fontweight='bold') ax3.set_xscale('log') ax3.set_yscale('log') # Add labels for i, row in df.iterrows(): if row['total_time(s)'] > 500 or row['call_count'] > 100000: ax3.annotate(row['function_name'][:10], (row['call_count'], row['cumulative_time(s)']), xytext=(5, 5), textcoords='offset points', fontsize=8) plt.colorbar(scatter, ax=ax3, label='Total Time (s)') # 4. Optimization Priority Matrix optimization_data = [ {'Project': 'one_qubit_base optimization', 'Impact': 9, 'Difficulty': 2, 'Benefit': '1-10%'}, {'Project': 'Matrix caching mechanism', 'Impact': 8, 'Difficulty': 5, 'Benefit': '5-15%'}, {'Project': 'Data type unification', 'Impact': 6, 'Difficulty': 2, 'Benefit': '3-8%'}, {'Project': 'Vectorized computation', 'Impact': 8, 'Difficulty': 6, 'Benefit': '10-20%'}, {'Project': 'GPU acceleration', 'Impact': 10, 'Difficulty': 9, 'Benefit': '30-50%'}, {'Project': 'Sparse matrix optimization', 'Impact': 7, 'Difficulty': 7, 'Benefit': '8-15%'}, {'Project': 'Parallel processing', 'Impact': 8, 'Difficulty': 8, 'Benefit': '15-25%'} ] opt_df = pd.DataFrame(optimization_data) # Draw priority matrix for i, row in opt_df.iterrows(): color = 'red' if row['Impact'] >= 8 and row['Difficulty'] <= 5 else 'orange' if row['Impact'] >= 7 else 'lightblue' ax4.scatter(row['Difficulty'], row['Impact'], s=200, alpha=0.7, c=color, edgecolors='black') ax4.annotate(row['Project'], (row['Difficulty'], row['Impact']), xytext=(5, 5), textcoords='offset points', fontsize=9) # Add quadrant labels ax4.axhline(y=7, color='gray', linestyle='--', alpha=0.5) ax4.axvline(x=5, color='gray', linestyle='--', alpha=0.5) ax4.text(2.5, 9, 'Quick Wins', fontsize=10, fontweight='bold', ha='center') ax4.text(7.5, 9, 'Core Optimizations', fontsize=10, fontweight='bold', ha='center') ax4.text(2.5, 5, 'Maintenance', fontsize=10, fontweight='bold', ha='center') ax4.text(7.5, 5, 'Long-term Planning', fontsize=10, fontweight='bold', ha='center') ax4.set_xlabel('Implementation Difficulty') ax4.set_ylabel('Expected Impact') ax4.set_title('Optimization Priority Matrix', fontweight='bold') ax4.set_xlim(0, 10) ax4.set_ylim(0, 10) ax4.grid(True, alpha=0.3) plt.tight_layout() plt.show()

🔍 调用栈分析可视化¶

In [6]:

# Call stack data
call_stack_data = {
    'Path': [
        'circuit_call → execute → execute_circuit → apply_gate → one_qubit_base',
        'terms_call → terms_call → cast → astype',
        'expectation → expectation → matmul → calculate_expectation_state → apply_gates'
    ],
    'Cumulative_Time': [805.8, 381.1, 1349],
    'Main_Bottleneck': ['one_qubit_base', 'cast/astype', 'matmul'],
    'Optimization_Potential': ['High', 'Medium', 'High']
}

call_df = pd.DataFrame(call_stack_data)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
fig.suptitle('Call Stack Depth Analysis', fontsize=16, fontweight='bold')

# 1. Call path time comparison
colors = ['#FF6B6B', '#4ECDC4', '#45B7D1']
bars = ax1.barh(call_df['Path'], call_df['Cumulative_Time'], color=colors, alpha=0.8)
ax1.set_title('Cumulative Time of Main Call Paths', fontweight='bold')
ax1.set_xlabel('Cumulative Time (seconds)')

# Add value labels
for i, v in enumerate(call_df['Cumulative_Time']):
    ax1.text(v + 20, i, f'{v:.1f}s', va='center', fontweight='bold')

# 2. Optimization potential assessment
potential_scores = {'High': 3, 'Medium': 2, 'Low': 1}
call_df['Potential_Score'] = call_df['Optimization_Potential'].map(potential_scores)

scatter = ax2.scatter(call_df['Cumulative_Time'], call_df['Potential_Score'], 
                     s=300, alpha=0.7, c=range(len(call_df)), 
                     cmap='viridis', edgecolors='black')

for i, row in call_df.iterrows():
    path_short = row['Path'].split(' → ')[-1]  # Only show the last function name
    ax2.annotate(f'{path_short}\n({row["Optimization_Potential"]})', 
                (row['Cumulative_Time'], row['Potential_Score']),
                xytext=(0, 10), textcoords='offset points', 
                ha='center', fontsize=10, fontweight='bold')

ax2.set_xlabel('Cumulative Time (seconds)')
ax2.set_ylabel('Optimization Potential')
ax2.set_title('Call Path Optimization Potential Assessment', fontweight='bold')
ax2.set_yticks([1, 2, 3])
ax2.set_yticklabels(['Low', 'Medium', 'High'])
ax2.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Call stack data call_stack_data = { 'Path': [ 'circuit_call → execute → execute_circuit → apply_gate → one_qubit_base', 'terms_call → terms_call → cast → astype', 'expectation → expectation → matmul → calculate_expectation_state → apply_gates' ], 'Cumulative_Time': [805.8, 381.1, 1349], 'Main_Bottleneck': ['one_qubit_base', 'cast/astype', 'matmul'], 'Optimization_Potential': ['High', 'Medium', 'High'] } call_df = pd.DataFrame(call_stack_data) fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8)) fig.suptitle('Call Stack Depth Analysis', fontsize=16, fontweight='bold') # 1. Call path time comparison colors = ['#FF6B6B', '#4ECDC4', '#45B7D1'] bars = ax1.barh(call_df['Path'], call_df['Cumulative_Time'], color=colors, alpha=0.8) ax1.set_title('Cumulative Time of Main Call Paths', fontweight='bold') ax1.set_xlabel('Cumulative Time (seconds)') # Add value labels for i, v in enumerate(call_df['Cumulative_Time']): ax1.text(v + 20, i, f'{v:.1f}s', va='center', fontweight='bold') # 2. Optimization potential assessment potential_scores = {'High': 3, 'Medium': 2, 'Low': 1} call_df['Potential_Score'] = call_df['Optimization_Potential'].map(potential_scores) scatter = ax2.scatter(call_df['Cumulative_Time'], call_df['Potential_Score'], s=300, alpha=0.7, c=range(len(call_df)), cmap='viridis', edgecolors='black') for i, row in call_df.iterrows(): path_short = row['Path'].split(' → ')[-1] # Only show the last function name ax2.annotate(f'{path_short}\n({row["Optimization_Potential"]})', (row['Cumulative_Time'], row['Potential_Score']), xytext=(0, 10), textcoords='offset points', ha='center', fontsize=10, fontweight='bold') ax2.set_xlabel('Cumulative Time (seconds)') ax2.set_ylabel('Optimization Potential') ax2.set_title('Call Path Optimization Potential Assessment', fontweight='bold') ax2.set_yticks([1, 2, 3]) ax2.set_yticklabels(['Low', 'Medium', 'High']) ax2.grid(True, alpha=0.3) plt.tight_layout() plt.show()

📈 优化效果预测¶

In [7]:

# Optimization effect prediction data
optimization_effects = {
    'Optimization_Phase': ['Current', 'Short-term(1-2 weeks)', 'Medium-term(1-2 months)', 'Long-term(3-6 months)'],
    'Expected_Performance_Improvement': [0, 10, 30, 55],
    'Memory_Optimization': [0, 20, 35, 50],
    'Implementation_Complexity': [0, 3, 6, 9]
}

effect_df = pd.DataFrame(optimization_effects)

fig, ax1 = plt.subplots(figsize=(12, 8))

# Create dual-axis chart
ax2 = ax1.twinx()

# Plot performance improvement and memory optimization
line1 = ax1.plot(effect_df['Optimization_Phase'], effect_df['Expected_Performance_Improvement'], 
                 'o-', linewidth=3, markersize=8, color='#FF6B6B', 
                 label='Performance Improvement (%)')
line2 = ax1.plot(effect_df['Optimization_Phase'], effect_df['Memory_Optimization'], 
                 's-', linewidth=3, markersize=8, color='#4ECDC4', 
                 label='Memory Optimization (%)')

# Plot implementation complexity
line3 = ax2.plot(effect_df['Optimization_Phase'], effect_df['Implementation_Complexity'], 
                 '^-', linewidth=3, markersize=8, color='#45B7D1', 
                 label='Implementation Complexity')

# Set chart properties
ax1.set_xlabel('Optimization Phase', fontsize=12, fontweight='bold')
ax1.set_ylabel('Optimization Effect (%)', fontsize=12, fontweight='bold', color='black')
ax2.set_ylabel('Implementation Complexity', fontsize=12, fontweight='bold', color='#45B7D1')
ax1.set_title('VQE Optimization Effect Prediction and Implementation Roadmap', fontsize=14, fontweight='bold')

# Add grid
ax1.grid(True, alpha=0.3)
ax1.set_ylim(0, 60)
ax2.set_ylim(0, 10)

# Add value labels
for i, (perf, mem, comp) in enumerate(zip(effect_df['Expected_Performance_Improvement'], 
                                         effect_df['Memory_Optimization'], 
                                         effect_df['Implementation_Complexity'])):
    ax1.text(i, perf + 2, f'{perf}%', ha='center', fontweight='bold', color='#FF6B6B')
    ax1.text(i, mem + 2, f'{mem}%', ha='center', fontweight='bold', color='#4ECDC4')
    ax2.text(i, comp + 0.3, f'{comp}', ha='center', fontweight='bold', color='#45B7D1')

# Combine legends
lines = line1 + line2 + line3
labels = [l.get_label() for l in lines]
ax1.legend(lines, labels, loc='upper left')

plt.tight_layout()
plt.show()

# Optimization effect prediction data optimization_effects = { 'Optimization_Phase': ['Current', 'Short-term(1-2 weeks)', 'Medium-term(1-2 months)', 'Long-term(3-6 months)'], 'Expected_Performance_Improvement': [0, 10, 30, 55], 'Memory_Optimization': [0, 20, 35, 50], 'Implementation_Complexity': [0, 3, 6, 9] } effect_df = pd.DataFrame(optimization_effects) fig, ax1 = plt.subplots(figsize=(12, 8)) # Create dual-axis chart ax2 = ax1.twinx() # Plot performance improvement and memory optimization line1 = ax1.plot(effect_df['Optimization_Phase'], effect_df['Expected_Performance_Improvement'], 'o-', linewidth=3, markersize=8, color='#FF6B6B', label='Performance Improvement (%)') line2 = ax1.plot(effect_df['Optimization_Phase'], effect_df['Memory_Optimization'], 's-', linewidth=3, markersize=8, color='#4ECDC4', label='Memory Optimization (%)') # Plot implementation complexity line3 = ax2.plot(effect_df['Optimization_Phase'], effect_df['Implementation_Complexity'], '^-', linewidth=3, markersize=8, color='#45B7D1', label='Implementation Complexity') # Set chart properties ax1.set_xlabel('Optimization Phase', fontsize=12, fontweight='bold') ax1.set_ylabel('Optimization Effect (%)', fontsize=12, fontweight='bold', color='black') ax2.set_ylabel('Implementation Complexity', fontsize=12, fontweight='bold', color='#45B7D1') ax1.set_title('VQE Optimization Effect Prediction and Implementation Roadmap', fontsize=14, fontweight='bold') # Add grid ax1.grid(True, alpha=0.3) ax1.set_ylim(0, 60) ax2.set_ylim(0, 10) # Add value labels for i, (perf, mem, comp) in enumerate(zip(effect_df['Expected_Performance_Improvement'], effect_df['Memory_Optimization'], effect_df['Implementation_Complexity'])): ax1.text(i, perf + 2, f'{perf}%', ha='center', fontweight='bold', color='#FF6B6B') ax1.text(i, mem + 2, f'{mem}%', ha='center', fontweight='bold', color='#4ECDC4') ax2.text(i, comp + 0.3, f'{comp}', ha='center', fontweight='bold', color='#45B7D1') # Combine legends lines = line1 + line2 + line3 labels = [l.get_label() for l in lines] ax1.legend(lines, labels, loc='upper left') plt.tight_layout() plt.show()

🎯 关键指标仪表板¶

In [8]:

# Create key metrics dashboard
fig = plt.figure(figsize=(16, 10))
fig.suptitle('VQE Performance Optimization Key Metrics Dashboard', fontsize=16, fontweight='bold')

# Create grid layout
gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)

# 1. Top bottleneck functions (top left large plot)
ax1 = fig.add_subplot(gs[0, :2])
top_bottleneck = df.nlargest(5, 'total_time(s)')
bars = ax1.bar(top_bottleneck['function_name'], top_bottleneck['total_time(s)'], 
               color=['#E74C3C', '#E67E22', '#F39C12', '#27AE60', '#3498DB'])
ax1.set_title('🔴 Top 5 Performance Bottlenecks', fontweight='bold', fontsize=14)
ax1.set_ylabel('Total Time (seconds)')
ax1.tick_params(axis='x', rotation=45)
for bar, value in zip(bars, top_bottleneck['total_time(s)']):
    ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 10, 
             f'{value:.1f}s', ha='center', va='bottom', fontweight='bold')

# 2. Optimization potential gauge (top right)
ax2 = fig.add_subplot(gs[0, 2])
ax2.set_xlim(0, 1)
ax2.set_ylim(0, 1)
ax2.axis('off')

# Draw gauge
angles = np.linspace(0, 2*np.pi, 100)

# Background arc
ax2.plot(0.5 + 0.3*np.cos(angles), 0.7 + 0.3*np.sin(angles), 'lightgray', linewidth=20)
ax2.plot(0.5 + 0.3*np.cos(angles[:75]), 0.7 + 0.3*np.sin(angles[:75]), '#4ECDC4', linewidth=20)
ax2.text(0.5, 0.3, '75%', ha='center', va='center', fontsize=24, fontweight='bold')
ax2.text(0.5, 0.1, 'Overall Optimization Potential', ha='center', va='center', fontsize=12)

# 3. Call frequency heatmap (middle left)
ax3 = fig.add_subplot(gs[1, 0])
call_freq_matrix = np.array([
    [605616, 797.2],  # one_qubit_base
    [81840, 301.4],   # terms.__call__
    [2046, 13.46],    # calculate_expectation_state
    [2046, 5.425],    # expectation
    [689622, 1.775]   # cast
])

# Normalize data for heatmap
normalized_matrix = call_freq_matrix / call_freq_matrix.max(axis=0)
im = ax3.imshow(normalized_matrix, cmap='Reds', aspect='auto')
ax3.set_xticks([0, 1])
ax3.set_xticklabels(['Call Count', 'Total Time'])
ax3.set_yticks(range(5))
ax3.set_yticklabels(['one_qubit_base', 'terms.__call__', 'calc_exp_state', 'expectation', 'cast'])
ax3.set_title('📊 Call Frequency vs Time Heatmap', fontweight='bold')
plt.colorbar(im, ax=ax3, fraction=0.046, pad=0.04)

# 4. ROI analysis (middle center)
ax4 = fig.add_subplot(gs[1, 1])
roi_data = {
    'Optimization_Item': ['Function Call Optimization', 'Matrix Caching', 'Data Type Unification', 'Vectorization', 'GPU Acceleration'],
    'Investment': [1, 3, 1, 5, 8],
    'Return': [2, 4, 2, 6, 10]
}
roi_df = pd.DataFrame(roi_data)
roi_df['ROI'] = roi_df['Return'] / roi_df['Investment']

bars = ax4.bar(roi_df['Optimization_Item'], roi_df['ROI'], 
               color=['#27AE60', '#3498DB', '#9B59B6', '#E67E22', '#E74C3C'])
ax4.set_title('💰 Optimization ROI Analysis', fontweight='bold')
ax4.set_ylabel('ROI (Return/Investment)')
ax4.tick_params(axis='x', rotation=45)
for bar, roi in zip(bars, roi_df['ROI']):
    ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, 
             f'{roi:.1f}x', ha='center', va='bottom', fontweight='bold')

# 5. Implementation timeline (middle right)
ax5 = fig.add_subplot(gs[1, 2])
timeline_data = {
    'Task': ['Function Optimization', 'Data Unification', 'Caching Mechanism', 'Vectorization', 'GPU Integration'],
    'Start_Week': [0, 0, 1, 3, 6],
    'Duration_Weeks': [1, 1, 2, 4, 8]
}
timeline_df = pd.DataFrame(timeline_data)

colors = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6']
for i, (_, row) in enumerate(timeline_df.iterrows()):
    ax5.barh(i, row['Duration_Weeks'], left=row['Start_Week'], height=0.6, 
            color=colors[i], alpha=0.8, label=row['Task'])
    ax5.text(row['Start_Week'] + row['Duration_Weeks']/2, i, 
             f'W{row["Start_Week"]+1}-{row["Start_Week"]+row["Duration_Weeks"]}', 
             ha='center', va='center', fontweight='bold', color='white')

ax5.set_yticks(range(len(timeline_df)))
ax5.set_yticklabels(timeline_df['Task'])
ax5.set_xlabel('Weeks')
ax5.set_title('📅 Implementation Timeline', fontweight='bold')
ax5.set_xlim(0, 14)
ax5.grid(True, alpha=0.3)

# 6. Risk assessment (bottom)
ax6 = fig.add_subplot(gs[2, :])
risk_data = {
    'Risk_Type': ['API Compatibility', 'Numerical Precision', 'Memory Safety', 'Performance Regression', 'Implementation Complexity'],
    'Probability': [0.3, 0.2, 0.1, 0.4, 0.6],
    'Impact': [0.8, 0.9, 0.7, 0.6, 0.5]
}
risk_df = pd.DataFrame(risk_data)

scatter = ax6.scatter(risk_df['Probability'], risk_df['Impact'], 
                     s=200, alpha=0.7, c=range(len(risk_df)), 
                     cmap='RdYlBu_r', edgecolors='black')

for i, row in risk_df.iterrows():
    ax6.annotate(row['Risk_Type'], (row['Probability'], row['Impact']), 
                xytext=(5, 5), textcoords='offset points', fontsize=10)

# Add risk zones
ax6.axhline(y=0.7, color='red', linestyle='--', alpha=0.5, label='High Risk Line')
ax6.axvline(x=0.5, color='orange', linestyle='--', alpha=0.5, label='High Probability Line')

ax6.set_xlabel('Probability')
ax6.set_ylabel('Impact')
ax6.set_title('⚠️ Risk Assessment Matrix', fontweight='bold')
ax6.set_xlim(0, 1)
ax6.set_ylim(0, 1)
ax6.grid(True, alpha=0.3)
ax6.legend()

plt.tight_layout()
plt.show()

# Create key metrics dashboard fig = plt.figure(figsize=(16, 10)) fig.suptitle('VQE Performance Optimization Key Metrics Dashboard', fontsize=16, fontweight='bold') # Create grid layout gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3) # 1. Top bottleneck functions (top left large plot) ax1 = fig.add_subplot(gs[0, :2]) top_bottleneck = df.nlargest(5, 'total_time(s)') bars = ax1.bar(top_bottleneck['function_name'], top_bottleneck['total_time(s)'], color=['#E74C3C', '#E67E22', '#F39C12', '#27AE60', '#3498DB']) ax1.set_title('🔴 Top 5 Performance Bottlenecks', fontweight='bold', fontsize=14) ax1.set_ylabel('Total Time (seconds)') ax1.tick_params(axis='x', rotation=45) for bar, value in zip(bars, top_bottleneck['total_time(s)']): ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 10, f'{value:.1f}s', ha='center', va='bottom', fontweight='bold') # 2. Optimization potential gauge (top right) ax2 = fig.add_subplot(gs[0, 2]) ax2.set_xlim(0, 1) ax2.set_ylim(0, 1) ax2.axis('off') # Draw gauge angles = np.linspace(0, 2*np.pi, 100) # Background arc ax2.plot(0.5 + 0.3*np.cos(angles), 0.7 + 0.3*np.sin(angles), 'lightgray', linewidth=20) ax2.plot(0.5 + 0.3*np.cos(angles[:75]), 0.7 + 0.3*np.sin(angles[:75]), '#4ECDC4', linewidth=20) ax2.text(0.5, 0.3, '75%', ha='center', va='center', fontsize=24, fontweight='bold') ax2.text(0.5, 0.1, 'Overall Optimization Potential', ha='center', va='center', fontsize=12) # 3. Call frequency heatmap (middle left) ax3 = fig.add_subplot(gs[1, 0]) call_freq_matrix = np.array([ [605616, 797.2], # one_qubit_base [81840, 301.4], # terms.__call__ [2046, 13.46], # calculate_expectation_state [2046, 5.425], # expectation [689622, 1.775] # cast ]) # Normalize data for heatmap normalized_matrix = call_freq_matrix / call_freq_matrix.max(axis=0) im = ax3.imshow(normalized_matrix, cmap='Reds', aspect='auto') ax3.set_xticks([0, 1]) ax3.set_xticklabels(['Call Count', 'Total Time']) ax3.set_yticks(range(5)) ax3.set_yticklabels(['one_qubit_base', 'terms.__call__', 'calc_exp_state', 'expectation', 'cast']) ax3.set_title('📊 Call Frequency vs Time Heatmap', fontweight='bold') plt.colorbar(im, ax=ax3, fraction=0.046, pad=0.04) # 4. ROI analysis (middle center) ax4 = fig.add_subplot(gs[1, 1]) roi_data = { 'Optimization_Item': ['Function Call Optimization', 'Matrix Caching', 'Data Type Unification', 'Vectorization', 'GPU Acceleration'], 'Investment': [1, 3, 1, 5, 8], 'Return': [2, 4, 2, 6, 10] } roi_df = pd.DataFrame(roi_data) roi_df['ROI'] = roi_df['Return'] / roi_df['Investment'] bars = ax4.bar(roi_df['Optimization_Item'], roi_df['ROI'], color=['#27AE60', '#3498DB', '#9B59B6', '#E67E22', '#E74C3C']) ax4.set_title('💰 Optimization ROI Analysis', fontweight='bold') ax4.set_ylabel('ROI (Return/Investment)') ax4.tick_params(axis='x', rotation=45) for bar, roi in zip(bars, roi_df['ROI']): ax4.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.1, f'{roi:.1f}x', ha='center', va='bottom', fontweight='bold') # 5. Implementation timeline (middle right) ax5 = fig.add_subplot(gs[1, 2]) timeline_data = { 'Task': ['Function Optimization', 'Data Unification', 'Caching Mechanism', 'Vectorization', 'GPU Integration'], 'Start_Week': [0, 0, 1, 3, 6], 'Duration_Weeks': [1, 1, 2, 4, 8] } timeline_df = pd.DataFrame(timeline_data) colors = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6'] for i, (_, row) in enumerate(timeline_df.iterrows()): ax5.barh(i, row['Duration_Weeks'], left=row['Start_Week'], height=0.6, color=colors[i], alpha=0.8, label=row['Task']) ax5.text(row['Start_Week'] + row['Duration_Weeks']/2, i, f'W{row["Start_Week"]+1}-{row["Start_Week"]+row["Duration_Weeks"]}', ha='center', va='center', fontweight='bold', color='white') ax5.set_yticks(range(len(timeline_df))) ax5.set_yticklabels(timeline_df['Task']) ax5.set_xlabel('Weeks') ax5.set_title('📅 Implementation Timeline', fontweight='bold') ax5.set_xlim(0, 14) ax5.grid(True, alpha=0.3) # 6. Risk assessment (bottom) ax6 = fig.add_subplot(gs[2, :]) risk_data = { 'Risk_Type': ['API Compatibility', 'Numerical Precision', 'Memory Safety', 'Performance Regression', 'Implementation Complexity'], 'Probability': [0.3, 0.2, 0.1, 0.4, 0.6], 'Impact': [0.8, 0.9, 0.7, 0.6, 0.5] } risk_df = pd.DataFrame(risk_data) scatter = ax6.scatter(risk_df['Probability'], risk_df['Impact'], s=200, alpha=0.7, c=range(len(risk_df)), cmap='RdYlBu_r', edgecolors='black') for i, row in risk_df.iterrows(): ax6.annotate(row['Risk_Type'], (row['Probability'], row['Impact']), xytext=(5, 5), textcoords='offset points', fontsize=10) # Add risk zones ax6.axhline(y=0.7, color='red', linestyle='--', alpha=0.5, label='High Risk Line') ax6.axvline(x=0.5, color='orange', linestyle='--', alpha=0.5, label='High Probability Line') ax6.set_xlabel('Probability') ax6.set_ylabel('Impact') ax6.set_title('⚠️ Risk Assessment Matrix', fontweight='bold') ax6.set_xlim(0, 1) ax6.set_ylim(0, 1) ax6.grid(True, alpha=0.3) ax6.legend() plt.tight_layout() plt.show()

📋 总结与建议¶

关键发现¶

1. one_qubit_base 是最大性能瓶颈，需要立即优化
2. 高频调用 累积效应显著，60万+次调用不可忽视
3. 矩阵运算 是计算密集型任务的主要开销
4. 数据类型转换 成为隐藏瓶颈，需要统一优化

优化建议¶

1. 立即实施 函数调用优化（1-10%性能提升）
2. 短期目标 建立矩阵缓存机制（5-15%性能提升）
3. 中期规划 实现向量化和并行计算（20-40%性能提升）
4. 长期愿景 GPU加速集成（50%+性能提升）

风险控制¶

• 保持API兼容性
• 确保数值精度
• 建立性能回归测试
• 渐进式部署验证