Medical image classification is essential for early diagnosis of diseases like cancer, neurological disorders, and infections. Traditional AI models require vast datasets and long training times — and they still struggle with image noise, low-contrast scans, or subtle patterns.

The Challenge:

• Medical images (e.g. MRIs, CT scans, X-rays) are high-dimensional and complex

• Subtle anomalies are often missed or misclassified

• Traditional models need large labeled datasets to learn effectively

• Diagnosis delays cost time, money, and lives

Quantum-Inspired Solution

What HessQ Does – 3 Key Benefits

Quantum-Like Feature Extraction

Uses quantum-inspired kernels to map image data into higher-dimensional spaces, improving pattern recognition.

Reduces training time with QUBO-based optimization — ideal for real-time analysis or resource-limited settings.

Detects anomalies even in noisy, low-resolution images — minimizing diagnostic errors.

Faster, Smaller Models

Improved Classification Accuracy

The Result:

• Higher precision: Improved sensitivity and specificity in detecting disease

• Lower training cost: Achieves high performance with smaller datasets

• Faster inference time: Suitable for real-time clinical support systems