# Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning

arXiv: https://arxiv.org/abs/2502.01495 · HTML: https://mlsanity.com/paper/2502.01495/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [Quantum Cognition Machine Learning for Forecasting Chromosomal Instability](https://mlsanity.com/paper/2506.03199/) (2025)
- [Quantum Geometry of Data](https://mlsanity.com/paper/2507.21135/) (2025)
- [Financial Bond Similarity Search Using Representation Learning](https://mlsanity.com/paper/2602.07020/) (2026)
- [What Makes a Peer? Valuation-Anchored Similarity in Private Markets](https://mlsanity.com/paper/2608.12594/) (2026)

## Abstract (arXiv)

We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/similarity among corporate bonds is particularly useful for a variety of practical applications in the trading of illiquid bonds, including the identification of similar tradable alternatives, pricing securities with relatively few recent quotes or trades, and explaining the predictions and performance of ML models based on their training data. Previous research has explored supervised similarity learning based on classical tree-based models in this context; here, we explore the application of the QCML paradigm for supervised distance metric learning in the same context, showing that it outperforms classical tree-based models in high-yield (HY) markets, while giving comparable or better performance (depending on the evaluation metric) in investment grade (IG) markets.