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<p>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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42452_2025_6638_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">\(11\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>11</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> in accuracy, while tensor networks offer equivalent performance with fewer parameters.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月6日

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  • Domenico Pomarico,
  • Alfonso Monaco,
  • Nicola Amoroso,
  • Loredana Bellantuono,
  • Antonio Lacalamita,
  • Marianna La Rocca,
  • Tommaso Maggipinto,
  • Ester Pantaleo,
  • Sabina Tangaro,
  • Sebastiano Stramaglia,
  • Roberto Bellotti

摘要

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