Quantum machine learning beyond kernel methods

Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models, remains limited. In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models. In particular, we show using tools from quantum information theory how data re-uploading circuits, an apparent outlier of this framework, can be efficiently mapped into the simpler picture of linear…

• EC
  European Commission

  Awards: 951821, 101055129
• VF
  Volkswagen Foundation

  Award: Az:97721
• ÖA
  Österreichischen Akademie der Wissenschaften
• AS
  Austrian Science Fund

  Awards: W1259, DK-ALM:W1259-N27, W1259-N27, F7102, SFB BeyondC F7102
• NO
  Nederlandse Organisatie voor Wetenschappelijk Onderzoek

  Awards: 024.003.037, 024.003