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Quantum machine learning for predicting molecular spectral properties

Quantum machine learning for predicting molecular spectral properties

Funding Agency
BASF (apply through Halo)
Funding Type
Career Researchers
Faculty
Postdocs
Graduate Students
Industry and Innovation
Deadline
Friday, January 31, 2025

The developed method should ultimately be applicable to different molecules, perform well on provided datasets, and be demonstrated on similar use cases.

Solutions of interest include:

  • Quantum computing algorithms that can be adapted to predict specific spectral properties of molecules

Our must-have requirements are:

  • Clear, high-level description of the quantum architecture
  • Strong rationale for potential quantum advantage
  • Provide relevant references that support your QML approach

What's out of scope:

  • Solutions that require external proprietary datasets.
  • Black-box approaches - we would like to understand the QML method for joint research.
  • Purely classical approaches – we are interested in quantum computing solutions.