Speaker
Description
Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce <span style="font-variant: small-caps;">Quiver</span> (QUantum-Informed Views for Enhanced Representations), a paradigm that enriches classical data-driven features with a quantum Fisher view: a geometrically motivated, basis-independent summary of higher-order correlations captured by a variational quantum circuit (VQC) trained to perform the same task. Unlike classical feature augmentation, the quantum Fisher information matrix encodes the intrinsic geometry of the learned quantum state manifold. This feature map, motivated by quantum information theory, is ordinarily non-trivial to model classically. However, it can reveal statistical structure that additional classical data or model capacity finds difficult to learn, making it a complementary modality. We demonstrate that <span style="font-variant: small-caps;">Quiver</span> improves standard performance metrics on two benchmark datasets from very different fields: the <span style="font-variant: small-caps;">JetClass</span> dataset for predicting jet flavor at the Large Hadron Collider (LHC), and the QM9 dataset for predicting molecule properties. The core contribution, however, is domain-agnostic: the quantum Fisher view can be fused into a broad class of model architectures via targeted modifications to the base architecture, to incorporate information about the quantum geometry of the problem. These results demonstrate that quantum-geometric features, extracted from simulated variational circuits, can deliver measurable value for standard machine learning tasks, well before the advent of fault-tolerant quantum hardware.