arXiv subjects

Tyler Cowan

Publications and source records attributed to Tyler Cowan.

3 recordsLinked to original sources

arXiv2026

High-rate Scalable Entanglement Swapping Between Remote Entanglement Sources on Deployed New York City Fibers

Entanglement swapping between photon pairs generated at physically separated nodes over telecommunication fiber infrastructure is an essential step towards the quantum internet, enabling applications such as quantum repeaters, blind quantum computing, distributed quantum computing, and distributed quantum sensing. However, successful networked entanglement swapping relies on generating indistinguishable pairs of photons and preserving them over deployed fibers. This has limited most previous demonstrations to laboratory settings or relied on sophisticated methods to maintain the necessary indistinguishability. Here, we demonstrate a scalable entanglement swapping experiment using naturally indistinguishable entanglement sources based on warm atomic vapor cells. Without sharing lasers or optical frequency references between nodes, nor the need for pulsing the sources, we achieve a swapping rate of nearly 500 pairs/s while maintaining the CHSH parameter above 2. Additionally, we demonstrate the scalability of our method by maintaining the quality of the entanglement swapping on 17.6-km of deployed fibers in NYC, relying on commercially available SPADs at the spoke nodes, SNSPDs at the hub and standard time-synchronization techniques. Our work paves the way for the practical deployment of large-scale hub-and-spoke quantum networks within cities and data centers.

quant-ph↗

arXiv2026

Hybrid superinductance with Al/InAs

We report microwave spectroscopy of Josephson junctions chains made from an epitaxial Al/InAs heterostructure. The chains exhibit superinductance, with characteristic wave impedance exceeding $R_{Q} = \hbar/(2e)^{2}$. The planar nature of the junctions results in a large plasma frequency, with no measurable deviations from ideal dispersion up to $12~\mathrm{GHz}$. Internal quality factors decrease sharply with frequency, which we describe with a simple loss model. The possibility of a loss mechanism intrinsic to the superconductor-semiconductor junction is considered.

cond-mat.mes-hall↗

arXiv2024

Machine learning analysis of structural data to predict electronic properties in near-surface InAs quantum wells

Semiconductor crosshatch patterns in thin film heterostructures form as a result of strain relaxation processes and dislocation pile-ups during growth of lattice mismatched materials. Due to their connection with the internal misfit dislocation network, these crosshatch patterns are a complex fingerprint of internal strain relaxation and growth anisotropy. Therefore, this mesoscopic fingerprint not only describes the residual strain state of a near-surface quantum well, but also could provide an indicator of the quality of electron transport through the material. Here, we present a method utilizing computer vision and machine learning to analyze AFM crosshatch patterns that exhibits this correlation. Our analysis reveals optimized electron transport for moderate values of $\lambda$ (crosshatch wavelength) and $\epsilon$ (crosshatch height), roughly 1 $\mu$m and 4 nm, respectively, that define the average waveform of the pattern. Simulated 2D AFM crosshatch patterns are used to train a machine learning model to correlate the crosshatch patterns to dislocation density. Furthermore, this model is used to evaluate the experimental AFM images and predict a dislocation density based on the crosshatch waveform. Predicted dislocation density, experimental AFM crosshatch data, and experimental transport characterization are used to train a final model to predict 2D electron gas mean free path. This model shows electron scattering is strongly correlated with elastic effects (e.g. dislocation scattering) below 200 nm $\lambda_{MFP}$.

cond-mat.mes-hall↗