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 $$11\%$$ in accuracy, while tensor networks offer equivalent performance with fewer parameters.

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Publication Details

Journal
Discover Applied Sciences
Published
2025-03-06
DOI
https://doi.org/10.1007/s42452-025-06638-6
Citations
4
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
9.92
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article

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

Nicola Amoroso, Alfonso Monaco, R. Bellotti, Loredana Bellantuono et al.
4 citations
Discover Applied Sciences
Quantum Computing Algorithms and Architecture
9.92
article

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

Nicola Amoroso, Alfonso Monaco, R. Bellotti, Loredana Bellantuono, Domenico Pomarico, Sabina Tangaro, Antonio Lacalamita, Ester Pantaleo, Marianna La Rocca, T. Maggipinto, Sebastiano Stramaglia
article en
4 citations

Abstract

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 $$11\%$$ in accuracy, while tensor networks offer equivalent performance with fewer parameters.

Discover Applied SciencesVol. 7(3)
Istituto Nazionale di Fisica Nucleare, Sezione di Bari (IT), University of Bari Aldo Moro (IT)
Openalex Percentile: Top 3%
Quantum Computing Algorithms and Architecture
9.92
DOIOpen Access PDFOpenAlex
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Emerging generalization advantage of quantum-inspired machine learning in the diagnosis of hepatocellular carcinoma — Nicola Amoroso, Alfonso Monaco, et al. · Discover Applied Sciences (2025) | TGRS Research Map | TGRS