# Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

arXiv: https://arxiv.org/abs/2506.03199 · HTML: https://mlsanity.com/paper/2506.03199/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning](https://mlsanity.com/paper/2409.12805/) (2024)
- [Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning](https://mlsanity.com/paper/2502.01495/) (2025)
- [Fully Automated CTC Detection, Segmentation and Classification for Multi-Channel IF Imaging](https://mlsanity.com/paper/2410.02988/) (2024)

## Built on by (most cited first, 2 of 2)

- [Quantum Geometry of Data](https://mlsanity.com/paper/2507.21135/) (2025)
- [Shallow-circuit Supervised Learning on a Quantum Processor](https://mlsanity.com/paper/2601.03235/) (2026)

## Abstract (arXiv)

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.