# Toward Quantum Utility in Finance: A Robust Data-Driven Algorithm for Asset Clustering

arXiv: https://arxiv.org/abs/2509.07766 · HTML: https://mlsanity.com/paper/2509.07766/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [SPONGE: A generalized eigenproblem for clustering signed networks](https://mlsanity.com/paper/1904.08575/) (2019)
- [Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation](https://mlsanity.com/paper/2311.12912/) (2023)
- [GCS-Q: Quantum Graph Coalition Structure Generation](https://mlsanity.com/paper/2212.11372/) (2022)
- [Qubit-efficient Variational Quantum Algorithms for Image Segmentation](https://mlsanity.com/paper/2405.14405/) (2024)
- [QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games](https://mlsanity.com/paper/2304.07218/) (2023)
- [Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market](https://mlsanity.com/paper/2406.10695/) (2024)
- [Quantum Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites](https://mlsanity.com/paper/2408.06007/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning structural balance of graphs from quantum spectral features](https://mlsanity.com/paper/2609.11736/) (2026)

## Abstract (arXiv)

Clustering financial assets based on return correlations is a fundamental task in portfolio optimization and statistical arbitrage. However, classical clustering methods often fall short when dealing with signed correlation structures, typically requiring lossy transformations and heuristic assumptions such as a fixed number of clusters. In this work, we apply the Graph-based Coalition Structure Generation algorithm (GCS-Q) to directly cluster signed, weighted graphs without relying on such transformations. GCS-Q formulates each partitioning step as a QUBO problem, enabling it to leverage quantum annealing for efficient exploration of exponentially large solution spaces. We validate our approach on both synthetic and real-world financial data, benchmarking against state-of-the-art classical algorithms such as SPONGE and k-Medoids. Our experiments demonstrate that GCS-Q consistently achieves higher clustering quality, as measured by Adjusted Rand Index and structural balance penalties, while dynamically determining the number of clusters. These results highlight the practical utility of near-term quantum computing for graph-based unsupervised learning in financial applications.