arXiv subjects

Philipp Elsässer

Publications and source records attributed to Philipp Elsässer.

3 recordsLinked to original sources

arXiv2026

Ultrafast configuration changes and anomalous diffusion of an aromatic adsorbate on rare-gas nanoparticles

Nanoparticles (NPs) exhibit tunable catalytic properties and serve as nanoreactors for controlled multimolecular chemistry. The kinetics and reactivity of such systems are critically governed by the surface binding configurations of adsorbates, their stochastic fluctuations, and the adsorbate mobility across the nanosurface. However, resolving these properties with sufficient structural, spatial, and temporal resolution remains a major experimental challenge. Here, we study phthalocyanine adsorbates on rare-gas clusters as a test case. By combining high-resolution two-dimensional electronic spectroscopy and molecular dynamics simulations, we reveal the configurational dynamics of the adsorbates and establish a direct relation between these dynamics and the nanoscale properties of the clusters. Our findings indicate sub-diffusive surface motion and trapping of the adsorbate within single surface facets. Such dynamical behavior seems unexpected considering the weak adsorbate-surface interaction and cluster temperatures close to the sublimation point. These results provide direct insight into the ultrafast binding dynamics of molecular adsorbates on nanoscale objects, which is critical for our understanding of the chemistry of such systems.

physics.chem-ph↗

arXiv2026

Quantum feature-map learning with reduced resource overhead

Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature-maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Learning via Analytic Iterative Reconstructions (Q-FLAIR), an algorithm that reduces quantum resource overhead in iterative feature-map circuit construction. It shifts workloads to a classical computer via partial analytic reconstructions of the quantum model, using only a few evaluations. For each probed gate addition to the ansatz, the simultaneous selection and optimization of the data feature and weight parameter is then entirely classical. Integrated into quantum neural network and quantum kernel support vector classifiers, Q-FLAIR shows state-of-the-art benchmark performance. Since resource overhead decouples from feature dimension, we train a quantum model on a real IBM device in only four hours, surpassing 90% accuracy on the full-resolution MNIST dataset (784 features, digits 3 vs 5). Such results were previously unattainable, as the feature dimension prohibitively drives hardware demands for fixed and search costs for adaptive ansätze. Furthermore, Q-FLAIR demonstrates de-quantization robustness against direct classical modeling, satisfying a benchmark rare in the literature and a necessary condition for potential quantum advantage. By rethinking feature-map learning beyond black-box optimization, this work takes a concrete step toward enabling quantum machine learning for real-world problems and near-term quantum computers.

quant-ph↗

arXiv2023

Optimizing the Structure of Acene Clusters

We present a study of the potential energy surface (PES) of anthracene, tetracene and pentacene clusters with up to 30 molecules. We have applied the basin-hopping Monte Carlo (BHMC) algorithm to clusters of acene molecules in order to find their lowest energy states. The acene molecules are described by the polymer-consistent force field - interface force field (PCFF-IFF). We present the structures with the lowest observed energy, and we discuss the relative stability and accessibility of structures corresponding to local energy minima.

physics.atm-clus↗