Performance Evaluation of the Tensor Train Sampler in ML QUBO-based ADMET Classification

Hadi Salloum, Kamil Sabbagh, Ruslan Lukin, Gleb Ryzhakov, Yaroslav Kholodov

Keywords: QUBO, Tensor Train Sampling, ADMET Classification, Quantum Annealing

TL;DR: Quantum annealing and tensor train sampling are compared for QUBO-based ADMET classification, with benchmarks highlighting TT sampling’s potential for enhanced drug discovery optimization

Abstract: Quantum Annealing (QA) on D-Wave’s Advantage system and Tensor Train (TT) sampling are compared for QUBO-based ADMET classification. QA-based methods (QSVM, QBoost) leverage quantum effects to escape local minima, while TT sampling employs low-rank decompositions for efficient high-dimensional data handling. Benchmarks highlight TT sampling’s potential for improved optimization in drug discovery.

Submission Number: 51