[2603.28294] Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows

Quantum Physics arXiv:2603.28294 (quant-ph) [Submitted on 30 Mar 2026] Title:Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows Authors:Kosuke Ito, Akira Tanji, Hiroshi Yano, Yudai Suzuki, Naoki Yamamoto View a PDF of the paper titled Learning from imperfect quantum data via unsupervised domain adaptation with classical shadows, by Kosuke Ito and 4 other authors View PDF Abstract:Learning from quantum data using classical machine learning models has emerged as a promising paradigm toward realizing quantum advantages. Despite extensive analyses on their performance, clean and fully labeled quantum data from the target domain are often unavailable in practical scenarios, forcing models to be trained on data collected under conditions that differ from those encountered at deployment. This mismatch highlights the need for new approaches beyond the common assumptions of prior work. In this work, we address this issue by employing an unsupervised domain adaptation framework for learning from imperfect quantum data. Specifically, by leveraging classical representations of quantum states obtained via classical shadows, we perform unsupervised domain adaptation entirely within a classical computational pipeline once measurements on the quantum states are executed. We numerically evaluate the framework on quantum phases of matter and entanglement classification tasks under realistic domain shifts. Across both tasks, our method outperforms ...