Quantum enhanced stratification of breast cancer: exploring quantum expressivity for real omics data

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 aim to assess whether quantum kernels (QK) could effectively classify subtypes of breast cancer (BC) patients based on molecular characteristics. We performed a 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 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.