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PreprintCC BYChemRxiv

Testing Small Parameterized Quantum Circuits for Molecular Property Prediction

  • Bhagya Sri Uddandam1,2
  • Chirag Girish Mokashi1,2
  • Jose Rodrigo Arizpe Figueroa1,2
  • Krishnakumar Vijayasankar1,2
  • Sharan Sandhu1,2
  • Sneha Paul1,2
  • Yun-Tang Lin1,2
  • Zixuan Jin1,2
  • Roberto dos Reis3,4
  • Anton V. Sinitskiy1,2,5
5 affiliations
  1. 1College of Professional Studies
  2. 2Northeastern University
  3. 3Department of Materials Science and Engineering
  4. 4Northwestern University
  5. 5ML LC
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Abstract

Parameterized quantum circuits (PQCs) are widely proposed as feature transformations in quantum machine learning (QML) pipelines. However, few molecular-QML studies combine strong classical baselines, repeated training runs, and controls that separate a quantum branch from its trainable anglegenerating encoder. We benchmark three-qubit PQCs combined with two pretrained molecular embeddings, MiniMol and CheMeleon, for lipophilicity and solubility prediction. Five model variants, including a classical baseline, two hybrid quantum-classical architectures, a quantum-only model, and a frozen-branch control, were evaluated on two datasets, two embeddings, and five random seeds, giving 100 runs. No tested quantum variant showed a statistically detectable improvement over the classical baseline after falsediscovery-rate correction across all 16 paired comparisons. The frozen-branch control also showed no detectable advantage over the classical baseline. This control freezes the full angle-generating quantum branch, not an independent set of variational circuit weights. A separate one-qubit depolarizing-noise sweep showed no detectable change in accuracy over the tested error range. Quantum variants required 18–37 times longer training time than the classical baseline on the same platform. These results set a benchmark for small parameterized QML layers applied to the pretrained molecular representations and ADME tasks examined here.

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Cite

BibTeXRIS
BibTeX
@misc{uddandam2026testing,
  title = {{Testing Small Parameterized Quantum Circuits for Molecular Property Prediction}},
  author = {Bhagya Sri Uddandam and Chirag Girish Mokashi and Jose Rodrigo Arizpe Figueroa and Krishnakumar Vijayasankar and Sharan Sandhu and Sneha Paul and Yun-Tang Lin and Zixuan Jin and Roberto dos Reis and Anton V. Sinitskiy},
  howpublished = {ChemRxiv},
  year = {2026},
  month = aug,
  doi = {10.26434/chemrxiv.15008061/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008061/v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Testing Small Parameterized Quantum Circuits for Molecular Property Prediction
AU  - Bhagya Sri Uddandam
AU  - Chirag Girish Mokashi
AU  - Jose Rodrigo Arizpe Figueroa
AU  - Krishnakumar Vijayasankar
AU  - Sharan Sandhu
AU  - Sneha Paul
AU  - Yun-Tang Lin
AU  - Zixuan Jin
AU  - Roberto dos Reis
AU  - Anton V. Sinitskiy
PY  - 2026
DA  - 2026/08/31
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15008061/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008061/v1
AB  - Parameterized quantum circuits (PQCs) are widely proposed as feature transformations in quantum machine learning (QML) pipelines. However, few molecular-QML studies combine strong classical baselines, repeated training runs, and controls that separate a quantum branch from its trainable anglegenerating encoder. We benchmark three-qubit PQCs combined with two pretrained molecular embeddings, MiniMol and CheMeleon, for lipophilicity and solubility prediction. Five model variants, including a classical baseline, two hybrid quantum-classical architectures, a quantum-only model, and a frozen-branch control, were evaluated on two datasets, two embeddings, and five random seeds, giving 100 runs. No tested quantum variant showed a statistically detectable improvement over the classical baseline after falsediscovery-rate correction across all 16 paired comparisons. The frozen-branch control also showed no detectable advantage over the classical baseline. This control freezes the full angle-generating quantum branch, not an independent set of variational circuit weights. A separate one-qubit depolarizing-noise sweep showed no detectable change in accuracy over the tested error range. Quantum variants required 18–37 times longer training time than the classical baseline on the same platform. These results set a benchmark for small parameterized QML layers applied to the pretrained molecular representations and ADME tasks examined here.
ER  -