TY - JOUR AU - Christensen, Anders S. AU - von Lilienfeld, O. Anatole PY - 2019/12/18 Y2 - 2024/03/28 TI - Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties JF - CHIMIA JA - Chimia VL - 73 IS - 12 SE - Scientific Articles DO - 10.2533/chimia.2019.1028 UR - https://www.chimia.ch/chimia/article/view/2019_1028 SP - 1028 AB - The identification and use of structure–property relationships lies at the heart of the chemical sciences. Quantum mechanics forms the basis for the unbiased virtual exploration of chemical compound space (CCS), imposing substantial compute needs if chemical accuracy is to be reached. In order to accelerate predictions of quantum properties without compromising accuracy, our lab has been developing quantum machine learning (QML) based models which can be applied throughout CCS. Here, we briefly explain, review, and discuss the recently introduced operator formalism which substantially improves the data efficiency for QML models of common response properties. ER -