Integration Examples

This section shows how to integrate QuScope with other tools and frameworks.

Integration with Matplotlib

Visualizing quantum circuits and results:

import quscope
import numpy as np
import matplotlib.pyplot as plt

# Create and encode image
image_data = np.random.rand(8, 8)
encoder = quscope.QuantumImageEncoder(image_size=(8, 8))
circuit = encoder.encode_amplitude_encoding(image_data)

# Visualize original image
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.imshow(image_data, cmap='viridis')
plt.title('Original Image')
plt.colorbar()

# Print circuit information
plt.subplot(1, 2, 2)
plt.text(0.1, 0.5, f'Quantum Circuit:\\n{circuit.num_qubits} qubits\\n{circuit.size()} gates')
plt.axis('off')
plt.show()

Integration with Jupyter Notebooks

Using QuScope in interactive Jupyter environments:

# Install in Jupyter
# !pip install quscope[viz]

import quscope
import numpy as np
from IPython.display import display, HTML

# Interactive workflow
def interactive_analysis(image_size=(4, 4)):
    # Generate sample data
    image_data = np.random.rand(*image_size)

    # Process with QuScope
    encoder = quscope.QuantumImageEncoder(image_size=image_size)
    circuit = encoder.encode_amplitude_encoding(image_data)

    # Display results
    print(f"🔬 Image size: {image_size}")
    print(f"⚛️  Quantum circuit: {circuit.num_qubits} qubits")
    print(f"🔧 Gates: {circuit.size()}")

    return circuit

# Run interactive analysis
result = interactive_analysis()

Integration with NumPy and SciPy

Leveraging scientific Python ecosystem:

import quscope
import numpy as np
from scipy import ndimage
from scipy.fft import fft2

# Complex image processing workflow
def quantum_enhanced_analysis(image_data):
    # Classical preprocessing with SciPy
    filtered_image = ndimage.gaussian_filter(image_data, sigma=1.0)

    # Fourier analysis
    fft_image = fft2(filtered_image)
    magnitude = np.abs(fft_image)

    # Quantum encoding
    encoder = quscope.QuantumImageEncoder(image_size=filtered_image.shape)
    circuit = encoder.encode_amplitude_encoding(filtered_image)

    return {
        'filtered': filtered_image,
        'fft_magnitude': magnitude,
        'quantum_circuit': circuit
    }

# Example usage
image_data = np.random.rand(8, 8)
results = quantum_enhanced_analysis(image_data)