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Poster
in
Workshop: Frontiers in Probabilistic Inference: learning meets Sampling

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

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

[ ] [ Project Page ]
[ OpenReview] 

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.

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