Data Science

Data Science

Overview

Comprehensive data science skill for the QWARD quantum computing project. Covers visualization, statistical analysis, and machine learning workflows for analyzing quantum circuit metrics and producing publication-quality results.

Quick Start

Plot QWARD Metrics

import matplotlib.pyplot as plt
import pandas as pd
from qward import Scanner
from qward.metrics import QiskitMetrics, ComplexityMetrics

scanner = Scanner(circuit=circuit)
scanner.add_strategy(QiskitMetrics(circuit))
scanner.add_strategy(ComplexityMetrics(circuit))
results = scanner.calculate_metrics()

# Plot complexity metrics
df = results['complexity']
fig, ax = plt.subplots(figsize=(10, 6))
df.plot(kind='bar', ax=ax)
ax.set_title('Circuit Complexity Metrics')
plt.savefig('qward/examples/img/complexity.png', dpi=300, bbox_inches='tight')

Statistical Comparison

from scipy import stats
import numpy as np

# Compare metrics across circuits
t_stat, p_value = stats.ttest_ind(metrics_circuit_a, metrics_circuit_b)
cohens_d = (np.mean(metrics_circuit_a) - np.mean(metrics_circuit_b)) / np.sqrt(
    (np.std(metrics_circuit_a)**2 + np.std(metrics_circuit_b)**2) / 2
)
print(f"t = {t_stat:.2f}, p = {p_value:.3f}, d = {cohens_d:.2f}")

Core Capabilities

1. Visualization (matplotlib + seaborn)

For creating plots, charts, and publication-ready figures.

Reference: See references/visualization.md for complete guidance on:

• Object-oriented matplotlib API (recommended over pyplot)
• All plot types: line, scatter, bar, histogram, heatmap, violin, radar
• Multi-panel figures with GridSpec and subplot_mosaic
• Seaborn statistical plots with automatic CIs
• Colorblind-safe palettes (Okabe-Ito, viridis, cividis)
• Publication export (PNG 300dpi, PDF/SVG vector, TIFF)
• Journal-specific styling (Nature, Science, Cell dimensions)
• 3D plots, contour plots, annotations

Quick patterns:

# Always use OO interface
fig, ax = plt.subplots(figsize=(10, 6), constrained_layout=True)
ax.plot(x, y, linewidth=2, label='data')
ax.set_xlabel('X Label (units)')
ax.set_ylabel('Y Label (units)')
ax.legend(frameon=False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.savefig('qward/examples/img/plot.png', dpi=300, bbox_inches='tight')

2. Statistical Analysis

For hypothesis testing, assumption checking, and reporting.

Reference: See references/statistics.md for complete guidance on:

• Test selection guide (parametric vs non-parametric)
• Assumption checking (normality, homogeneity, linearity)
• t-tests, ANOVA, chi-square, Mann-Whitney, Kruskal-Wallis
• Effect sizes (Cohen's d, eta-squared, Cramer's V) with CIs
• Power analysis and sample size calculations
• Multiple comparison corrections (Bonferroni, FDR)
• Bayesian alternatives (Bayes Factors, credible intervals)
• APA-style reporting templates

3. Statistical Modeling (statsmodels)

For regression, GLMs, time series, and econometric analysis.

Reference: See references/modeling.md for complete guidance on:

• Linear regression (OLS, WLS, GLS) with diagnostics
• Generalized linear models (logistic, Poisson, Gamma)
• Time series (ARIMA, SARIMAX, VAR, exponential smoothing)
• Formula API (R-style): smf.ols('y ~ x1 + C(group)', data=df)
• Robust standard errors (HC, HAC, cluster-robust)
• Model comparison (AIC, BIC, likelihood ratio tests)
• Residual diagnostics and influence analysis

4. Machine Learning (scikit-learn)

For classification, regression, clustering, and dimensionality reduction.

Reference: See references/machine-learning.md for complete guidance on:

• Pipelines and ColumnTransformer for production workflows
• Preprocessing (StandardScaler, OneHotEncoder, imputation)
• Supervised learning (RandomForest, GradientBoosting, SVM, KNN)
• Unsupervised learning (KMeans, DBSCAN, PCA, t-SNE, UMAP)
• Cross-validation and hyperparameter tuning (GridSearchCV)
• Metrics (classification_report, ROC AUC, silhouette score)
• Feature importance and model interpretability

QWARD-Specific Workflows

Analyzing Scanner Output

from qward import Scanner
from qward.metrics import QiskitMetrics, ComplexityMetrics
import pandas as pd

scanner = Scanner(circuit=circuit)
scanner.add_strategy(QiskitMetrics(circuit))
scanner.add_strategy(ComplexityMetrics(circuit))
results = scanner.calculate_metrics()

# Each value is a DataFrame
for metric_name, df in results.items():
    print(f"\n{metric_name}:")
    print(df.describe())

Comparing Circuits

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Collect metrics across circuits
all_metrics = []
for name, circuit in circuits.items():
    scanner = Scanner(circuit=circuit)
    scanner.add_strategy(ComplexityMetrics(circuit))
    result = scanner.calculate_metrics()
    df = result['complexity']
    df['circuit'] = name
    all_metrics.append(df)

combined = pd.concat(all_metrics, ignore_index=True)

# Visualize
fig, ax = plt.subplots(figsize=(10, 6))
sns.boxplot(data=combined, x='circuit', y='gate_density', ax=ax)
ax.set_ylabel('Gate Density')
sns.despine()
plt.savefig('qward/examples/img/comparison.png', dpi=300, bbox_inches='tight')

Correlation Analysis

import seaborn as sns
import numpy as np

# Correlation between complexity metrics
corr = df_metrics.corr(method='spearman')
mask = np.triu(np.ones_like(corr, dtype=bool))

fig, ax = plt.subplots(figsize=(8, 6))
sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
            cmap='RdBu_r', center=0, square=True, ax=ax)
plt.savefig('qward/examples/img/correlation.png', dpi=300, bbox_inches='tight')

Best Practices

Visualization

• Use OO interface (fig, ax = plt.subplots()) for production code
• Use constrained_layout=True to prevent overlapping
• Use colorblind-friendly palettes (viridis, cividis, Okabe-Ito)
• Save images to qward/examples/img/ per project convention
• 300 DPI for publications, vector (PDF/SVG) when possible

Statistics

• Always check assumptions before interpreting tests
• Report effect sizes with confidence intervals, not just p-values
• Use non-parametric tests when normality is violated
• Correct for multiple comparisons when testing many hypotheses
• Use Bayesian methods when you need evidence for the null

Machine Learning

• Always use Pipelines to prevent data leakage
• Use stratified splits for classification
• Set random_state for reproducibility
• Scale features for SVM, KNN, neural networks (not for trees)
• Report cross-validated metrics, not just train/test

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