Author

S. Johri

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Preprint Sep 2026

Discretization-Aware Fine-Tuning for Quantum Machine Learning with Chemical Foundation Models

This work introduces discretization-aware fine-tuning (DAFT), a method that adapts a pre-trained chemical foundation model to produce representations that remain informative after quantization, and evaluates both quantum and classical models under a controlled setting in which they receive identical discretized bit-str...

Shunji Matsuura, S. Johri · 0 citations

Preprint Aug 2026

Benchmarking Quantum and Classical Machine Learning Models on Oncological Data

This work develops a methodology for fair benchmarking of quantum and classical machine learning models, based on the Red Cedar quantum machine learning and resource estimation framework and AutoML-optimized classical neural networks, and finds no evidence of quantum advantage.

Sydney Leither, Thomas Lubinski, Michael Kubal et al. · 0 citations