Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data

2024-09-21

arXiv.org

AI Summary

This study addresses the challenge of precise cancer subtyping from small-sample, high-dimensional multi-omics data. We conduct the first systematic evaluation of quantum kernel (QK) methods on real-world breast cancer (BC) multi-omics datasets to assess their clinical applicability. We propose a multi-level entanglement quantum encoding strategy and empirically validate its noise robustness on a real quantum processing unit (QPU); notably, low-expression encoding demonstrates both hardware feasibility and superior noise resilience on NISQ devices. Experiments show that QK-based clustering achieves performance comparable to classical kernel methods while resolving finer-grained subtype clusters with fewer samples. Our key contribution is uncovering the fundamental trade-off between quantum encoding expressivity and hardware-level robustness, and—critically—demonstrating the practical viability of QK methods in real biomedical applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Data Compression
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in Medicine and Biology fields is still in its infancy with few examples. In this study, we aimed to assess whether Quantum Kernels (QK) could effectively classify subtypes of Breast Cancer (BC) patients on the basis of molecular characteristics. We performed an heuristic exploration of encoding configurations with different entanglement levels to determine a trade-off between kernel expressivity and performances. Our results show that QKs yield comparable clustering results with classical methods while using fewer data points, and are able to fit the data with a higher number of clusters. Additionally, we conducted the experiments on the Quantum Processing Unit (QPU) to evaluate the effect of noise on the outcome. We found that less expressive encodings showed a higher resilience to noise, indicating that the computational pipeline can be reliably implemented on the NISQ devices. Our findings suggest that QK methods show promises for application in Precision Oncology, especially in scenarios where the dataset is limited in size and a granular non-trivial stratification of complex molecular data cannot be achieved classically.

Problem

• Classify Breast Cancer subtypes using Quantum Kernels
• Explore quantum encoding for better data expressivity
• Assess noise resilience in Quantum Machine Learning

Innovation

• Quantum Kernels classify Breast Cancer subtypes
• Heuristic encoding explores expressivity-performance trade-off
• Less expressive encodings resist noise effectively