quantum-medical-image-encoding

name	quantum-medical-image-encoding
category	quantum-medicine
description	Quantum image encoding and compression methodology for medical imaging using Fourier-based methods. Reduces quantum gate requirements by factor of 4+ compared to existing approaches. Based on arXiv:2505.06471
activation	quantum medical imaging, quantum image encoding, quantum compression, QIMP, Fourier quantum, medical image quantum
arxiv_id	2505.06471
authors	Taehee Ko, Inho Lee, Hyeong Won Yu
created	2025-06-10
tags	["quantum-imaging","medical-imaging","quantum-compression","fourier-transform","gate-reduction"]

Quantum Medical Image Encoding and Compression

Overview

This skill provides a methodology for encoding classical medical images into quantum circuits with significantly reduced gate complexity, using Fourier-based techniques. The approach reduces quantum gates by at least 4x compared to existing encoding methods, making large-scale quantum medical imaging computationally feasible on near-term quantum hardware.

Core Methodology

1. Fourier-Based Quantum Image Encoding

Traditional quantum image encoding methods require approximately 2× the number of gates as pixels in the image. This Fourier-based approach reduces gate requirements by:

• Decomposing images into frequency domain using Fourier transforms
• Encoding frequency coefficients instead of pixel values
• Exploiting sparsity in frequency representation of medical images
• Achieving at least 4× gate reduction compared to direct pixel encoding

2. Compression Techniques

Two complementary compression methods:

2.1 Frequency Domain Compression

• Discard high-frequency components below quality threshold
• Maintain diagnostic quality while reducing qubit requirements
• Achieves additional 2-4× compression with negligible quality loss

2.2 Pre-processing Optimization

• Apply classical pre-processing to reduce quantum circuit depth
• Optimize measurement strategy for medical features
• Reduce overall quantum resource requirements

3. Validation Framework

• Tested on 1024×1024 high-quality medical images
• Validated with BABA (Bilateral Axillo-Breast Approach) robotic thyroidectomy surgical images
• Quality metrics: PSNR, SSIM, gate count, circuit depth
• Demonstrated feasibility for large-scale medical imaging applications

Implementation Steps

Step 1: Image Pre-processing

import numpy as np
from scipy.fft import fft2, ifft2

def prepare_medical_image(image_path, threshold=0.01):
    """Load and prepare medical image for quantum encoding"""
    # Load and normalize image
    img = load_and_normalize(image_path)
    
    # Apply 2D Fourier transform
    freq_domain = fft2(img)
    
    
    freq_domain[np.(freq_domain) < threshold * np.(np.(freq_domain))] = 
    
     freq_domain