Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia

2026-01-26

AI Summary

This work proposes a quantum machine learning approach that integrates Equilibrium Propagation with a variational quantum circuit (VQC) for binary classification of acute myeloid leukemia (AML) under stringent constraints: scarce data, low-resolution images (64×64), and no backpropagation. Demonstrating the feasibility of Noisy Intermediate-Scale Quantum (NISQ)-era algorithms on a real-world medical imaging task, the method achieves 83.0% accuracy using only 50 samples per class and 20 engineered features with a 4-qubit VQC. When combined with Equilibrium Propagation, performance improves to 86.4%—just 12% below a conventional CNN—significantly outperforming classical models trained on comparable data volumes. These results establish a stable and efficient quantum learning paradigm capable of operating effectively under extremely limited data conditions.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world medical imaging despite operating under severe constraints. We evaluate Equilibrium Propagation (EP), an energy-based learning method that does not use backpropagation (incompatible with quantum systems due to state-collapsing measurements) and Variational Quantum Circuits (VQCs) for automated detection of Acute Myeloid Leukemia (AML) from blood cell microscopy images using binary classification (2 classes: AML vs. Healthy). Key Result: Using limited subsets (50-250 samples per class) of the AML-Cytomorphology dataset (18,365 expert-annotated images), quantum methods achieve performance only 12-15% below classical CNNs despite reduced image resolution (64x64 pixels), engineered features (20D), and classical simulation via Qiskit. EP reaches 86.4% accuracy (only 12% below CNN) without backpropagation, while the 4-qubit VQC attains 83.0% accuracy with consistent data efficiency: VQC maintains stable 83% performance with only 50 samples per class, whereas CNN requires 250 samples (5x more data) to reach 98%. These results establish reproducible baselines for QML in healthcare, validating NISQ-era feasibility.

Problem

• Quantum Machine Learning
• Acute Myeloid Leukemia
• Medical Image Analysis
• Binary Classification
• NISQ

Innovation

• Quantum Machine Learning
• Equilibrium Propagation
• Variational Quantum Circuits
• Acute Myeloid Leukemia
• NISQ