Noah L. Wach | Searcharxiv

arXiv2026

Bringing Dark States to Light after Frustrated Tunneling Ionization

High-harmonic generation (HHG) in gases is typically regarded as a sub-laser-cycle process, well described by the semiclassical three-step model. In this work, we report the extreme-ultraviolet (XUV) emission observed in an HHG experiment arising from a longer temporal process associated with dark states populated through frustrated tunneling ionization. The emission exhibits a larger spatial divergence than typical high-order harmonics, resembling the polarization decay of optically bright states. Supported by the time-frequency analysis of the theoretical calculations, we show that the emission originates from the nonradiative capture of the tunneling electrons into excited dark states of the neutral atom, followed by Floquet-like laser-dressing effects induced by the extended tail of the driving pulse. Our results reveal a largely unexplored pathway for XUV generation through laser-dressed dark states, thereby enabling the enhancement of coherent XUV emission at targeted wavelengths with high repetition rates.

physics.atom-ph↗

arXiv2023

Data re-uploading with a single qudit

Quantum two-level systems, i.e. qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantum systems constitute a promising alternative and are increasingly explored in theory and practice. Here, we explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context. We formulate classification and regression problems with the data re-uploading approach and demonstrate that a quantum circuit operating on a single qudit is able to successfully learn highly non-linear decision boundaries of classification problems such as the MNIST digit recognition problem. We demonstrate that the performance strongly depends on the relation between the qudit states representing the labels and the structure of labels in the training data set. Such a bias can lead to substantial performance improvement over qubit-based circuits in cases where the labels, the qudit states and the operators employed to encode the data are well-aligned. Furthermore, we elucidate the influence of the choice of the elementary operators and show that a squeezing operator is necessary to achieve good performances. We also show that there exists a trade-off for qudit systems between the number of circuit-generating operators in each processing layer and the total number of layers needed to achieve a given accuracy. Finally, we compare classification results from numerically exact simulations and their equivalent implementation on actual IBM quantum hardware. The findings of our work support the notion that qudit-based algorithms exhibit attractive traits and constitute a promising route to increasing the computational capabilities of quantum machine learning approaches.

quant-ph↗