arXiv subjects

Alexander Zappacosta

Publications and source records attributed to Alexander Zappacosta.

3 recordsLinked to original sources

arXiv2025

Wavelength-Dependent Electrical Readout of Spin Ensembles in a Thin-Film SiC-on-Insulator Platform

We report electrical spin state readout and coherent control of an ensemble ($\sim$540) of silicon vacancies ($\mathrm{V}_{\mathrm{Si}}^{-}$) in a silicon carbide-on-insulator (SiCOI) platform, with excitation wavelengths from 780 to 990 nm, demonstrating for the first time spin state readout well beyond the zero phonon line of the V2 $\mathrm{V}_{\mathrm{Si}}^{-}$. By implementing photoelectrical detection of magnetic resonance in thin-film SiCOI, we merge a scalable spin readout technique requiring no collection optics, together with a promising platform for future scalable and CMOS-compatible integrated photonics. Furthermore, we provide a comparison of optical and electrical readout between bulk silicon carbide (SiC) and thin-film SiCOI, revealing that our thin-film processing has a measured $T_2$ coherence time of $\approx 7 \mu$s , similar to that in the bulk SiC. These results extend the capabilities of SiCOI toward electronic and spin-based devices for scalable quantum technologies over a wide range of excitation wavelengths.

quant-ph↗

arXiv2022

Picosecond Pulsed Squeezing in Thin-Film Lithium Niobate Strip-Loaded Waveguides at Telecommunication Wavelengths

Achieving high level of pulsed squeezing, in a platform which offers integration and stability, is a key requirement for continuous-variable quantum information processing. Typically highly squeezed states are achieved with narrow band optical cavities and bulk crystals, limiting scalability. Using single-pass parametric down conversion in an integrated optical device, we demonstrate quadrature squeezing of picosecond pulses in a thin-film lithium niobate strip-loaded waveguide. For on-chip peak powers of less than 0.3 W, we measure up to -0.33$\pm$0.07 dB of squeezing with an inferred on-chip value of -1.7$\pm$0.4 dB. This work highlights the potential of the strip-loaded waveguide platform for broadband squeezing applications and the development of photonic quantum technologies.

quant-ph↗

arXiv2021

Dynamic compensation of stray electric fields in an ion trap using machine learning and adaptive algorithm

Surface ion traps are among the most promising technologies for scaling up quantum computing machines, but their complicated multi-electrode geometry can make some tasks, including compensation for stray electric fields, challenging both at the level of modeling and of practical implementation. Here we demonstrate the compensation of stray electric fields using a gradient descent algorithm and a machine learning technique, which trained a deep learning network. We show automated dynamical compensation tested against induced electric charging from UV laser light hitting the chip trap surface. The results show improvement in compensation using gradient descent and the machine learner over manual compensation. This improvement is inferred from an increase of the fluorescence rate of 78% and 96% respectively, for a trapped $^{171}$Yb$^+$ ion driven by a laser tuned to -7.8 MHz of the $^2$S$_{1/2}\leftrightarrow^2$P$_{1/2}$ Doppler cooling transition at 369.5 nm.

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