Electronic-Structure-Informed Many-Body Encoding for Quantum Machine Learning

arXiv:2610.40840v1 Announce Type: new Abstract: Data encoding is a central challenge in quantum machine learning (QML), as it determines how classical information is mapped onto quantum states and influences the expressive power and efficiency of parameterized quantum circuits. Here, we introduce a physics-informed quantum encoding strategy that exploits the intrinsic many-body structure of electronic systems to establish a direct correspondence between the body order of input descriptors and the locality of quantum gates. Specifically, $n$-body electronic descriptors obtained from a single mean-field electronic-structure calculation are encoded through $n$-qubit gates, yielding a systematic hierarchy of quantum circuits with increasing many-body expressivity.