arXiv subjects

David López-Núñez

Publications and source records attributed to David López-Núñez.

3 recordsLinked to original sources

arXiv2025

Magnetic penetration depth of Aluminum thin films

We present a study of the superconducting penetration depth $λ$ in aluminum thin films of varying thickness. The range of thicknesses chosen spans from the thin-film regime to the regime approaching bulk behavior. The penetration depths observed range from $λ= 163.3\pm0.4~\rm{nm}$ for the thinnest $20~\rm{nm}$ samples down to $λ= 53.6\pm0.4~\rm{nm}$ for the $200~\rm{nm}$-thick ones. In order to accurately determine $λ$, we performed complementary measurements using the frequency of superconducting $LC$ resonators as well as the resistance of normal-state meanders. Both methods yield comparable results, providing a well-characterized set of values of $λ$ in aluminum in the relevant range for applications in fields such as quantum computing and microwave radiation detector technologies.

cond-mat.supr-con↗

arXiv2024

Superconducting nitridized-aluminum thin films

We report the direct observation of superconductivity in nitridized-aluminum thin films. The films are produced by sputtering deposition of aluminum in a controlled mixture of nitrogen diluted in argon. The concentration of applied nitrogen directly determines the properties of the superconducting thin films. We observe samples displaying critical temperatures up to 3.38$\pm$0.01K and resilience to in-plane magnetic fields well above 1T, with good reproducibility of the results. This work represents an unambiguous demonstration of tunable superconductivity in aluminum-based nitridized thin films. Our results put forward nitridized aluminum as a promising material to be employed in superconducting quantum circuits for quantum technology applications.

cond-mat.supr-con↗

arXiv2021

One qubit as a Universal Approximant

A single-qubit circuit can approximate any bounded complex function stored in the degrees of freedom defining its quantum gates. The single-qubit approximant presented in this work is operated through a series of gates that take as their parameterization the independent variable of the target function and an additional set of adjustable parameters. The independent variable is re-uploaded in every gate while the parameters are optimized for each target function. The output state of this quantum circuit becomes more accurate as the number of re-uploadings of the independent variable increases, i. e., as more layers of gates parameterized with the independent variable are applied. In this work, we provide two different proofs of this claim related to both the Fourier series and the Universal Approximation Theorem for Neural Networks, and we benchmark both methods against their classical counterparts. We further implement a single-qubit approximant in a real superconducting qubit device, demonstrating how the ability to describe a set of functions improves with the depth of the quantum circuit. This work shows the robustness of the re-uploading technique on Quantum Machine Learning.

quant-ph↗