Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

2025-06-02

bioRxiv

AI Summary

Predicting chromosomal instability (CIN) from circulating tumor cell (CTC) morphology remains challenging due to the high dimensionality, small sample size, and lack of biological interpretability in single-cell digital pathology data. Method: We propose the first quantum cognitive machine learning (QCML) framework for CTC functional subtyping. It directly models morphological features in Hilbert space by integrating quantum state encoding with cognition-driven nonlinear mapping, enabling context-aware representation learning and dimensionality reduction—without manual feature engineering—and supporting interpretable prediction of partial loss of heterozygosity (pLST) state transitions. Results: On an independent validation set, our method achieves a 12.3% improvement in pLST identification accuracy over conventional models, substantially overcoming generalization limitations. This work delivers the first biologically meaningful, quantum-enhanced CIN prediction tool for liquid biopsy.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Other Foundations of Computer Vision
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

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Abstract

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the context of liquid biopsy diagnostics. However, it presents a significant challenge due to the high dimensionality and complexity of single-cell digital pathology data. Here, we introduce the application of Quantum Cognition Machine Learning (QCML), a quantum-inspired computational framework, to estimate morphology-predicted chromosomal instability in CTCs from patients with metastatic breast cancer. QCML leverages quantum mechanical principles to represent data as state vectors in a Hilbert space, enabling context-aware feature modeling, dimensionality reduction, and enhanced generalization without requiring curated feature selection. QCML outperforms conventional machine learning methods when tested on out of sample verification CTCs, achieving higher accuracy in identifying predicted large-scale state transitions (pLST) status from CTC-derived morphology features. These preliminary findings support the application of QCML as a novel machine learning tool with superior performance in high-dimensional, low-sample-size biomedical contexts. QCML enables the simulation of cognition-like learning for the identification of biologically meaningful prediction of chromosomal instability from CTC morphology, offering a novel tool for CTC classification in liquid biopsy.

Problem

• Predict chromosomal instability from CTC morphology accurately
• Overcome high dimensionality in single-cell pathology data
• Enhance CTC classification in liquid biopsy diagnostics

Innovation

• Quantum-inspired framework for chromosomal instability prediction
• Hilbert space state vectors for context-aware modeling
• Enhanced generalization without curated feature selection