When do quantum views help? Complementary classical-quantum representations in multi-view fusion

Key points

• Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity.1
• Research includes comparisons with classical machine learning algorithms and the development of sequential hybrid pipelines.1
• Our classical-quantum multi-view framework preserves the full classical input alongside simulated quantum features.1
• These findings support evaluating quantum representations for their complementary predictive contribution and matching the representation and fusion strategy to the target relationships.1

Sources (1)

• [1]When do quantum views help? Complementary classical-quantum representations in multi-view fusion
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 07:15 AM

  We propose that fusion can improve prediction when classical and quantum representations capture complementary predictive relationships.

  Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity.

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