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Parametrized Quantum Circuit Learning for Quantum Chemical Applications.

Grier M Jones1,2, Viki Kumar Prasad1,2, Ulrich Fekl2

  • 1The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, OntarioM5S 3G4, Canada.

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|March 12, 2026
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This summary is machine-generated.

Parametrized quantum circuits (PQCs) show potential in quantum machine learning (QML) for chemistry problems. However, applying PQCs to chemically relevant data sets remains challenging, even with current quantum hardware.

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Area of Science:

  • Quantum Machine Learning (QML)
  • Computational Chemistry

Background:

  • Parametrized quantum circuits (PQCs) are a hybrid framework in QML.
  • Limited exploration of PQCs on quantum chemistry data sets exists.

Purpose of the Study:

  • Investigate PQC benefits and limitations on chemically relevant data.
  • Evaluate PQC performance using various encoding strategies and ansätze.

Main Methods:

  • Constructed 168 PQCs (14 encoding, 12 ansätze) for 5 and 16 qubits.
  • Used state-vector simulations to analyze circuit structure, depth, and training set size.
  • Assessed best PQCs on noisy simulations and real quantum hardware.

Main Results:

  • PQCs face challenges with chemically relevant data compared to classical methods.
  • Circuit structure, depth, and training data significantly impact PQC performance.
  • Real quantum hardware performance is evaluated.

Conclusions:

  • PQCs are not yet a straightforward solution for all quantum chemistry problems.
  • Further research is needed to optimize PQCs for complex chemical tasks.
  • Bridging the gap between classical and quantum approaches for chemistry is ongoing.