Emerging generalization advantage of quantum-inspired machine learning in the diagnosis of hepatocellular carcinoma.

Research into
quantum advantage is increasingly taking on an interdisciplinary character.
In particular, quantum machine learning shows promising generalization
capabilities, which we have exploited in the classification of
hepatocellular carcinoma tissue based on microarray gene expressions. By
using previously characterized genetic communities, we minimize the
computational complexity associated with the number of qubits, enabling the
execution of quantum-inspired algorithms on classical machines. We consider
two categories of such algorithms: parameterized quantum circuits (PQC) and
tensor networks. The variational optimization of PQCs achieves better
accuracy than classical counterparts on the independent test set, reaching
an advantage equal to in accuracy, while tensor networks offer equivalent
performance with fewer parameters.

https://link.springer.com/article/10.1007/s42452-025-06638-6