arXiv subjects

Collin N. Muniz

Publications and source records attributed to Collin N. Muniz.

4 recordsLinked to original sources

arXiv2026

Elimination of Flux Trapping in Superconducting Circuits in Ambient Magnetic Fields

Superconductor digital electronics and quantum computing with superconducting qubits are promising next-generation computing technologies. When cooled down or operated in the presence of a nonzero background magnetic field $B_r$, superconducting thin films comprising the circuits can trap magnetic vortices that can degrade circuit or qubit performance. In this work, we report a practical solution for eliminating flux trapped during cooldown in ambient magnetic fields, $B_r\leq 60$ $\upmu$T, based on controlled local thermal gradients and moats, etched holes in the superconducting films of the circuit. Thermal gradients created by integrated on-chip resistive heaters move vortices towards the moats, where they become trapped away from circuitry regions and pinning sites. Using magnetic imaging and electrical circuit readout, we demonstrate that this approach is capable of removing magnetic flux trapped during field cooling and magnetic flux nucleated by circuit operation. If used in an environment with basic magnetic shielding, this solution is capable of suppressing all magnetic flux in a large-scale circuit, overcoming one of the long-standing challenges preventing high-performance scalable computing using superconductors.

cond-mat.supr-con↗

arXiv2026

Mitigation of Magnetic Flux Trapping in Superconducting Electronics Using Moats

Magnetic flux (vortex) trapping remains a major obstacle to very large scale integration in superconducting electronics. Moats -- etched regions in circuit layers placed in ground planes and around critical circuitry -- offer a simple passive approach to sequester flux. Here, we systematically examine the effectiveness of moat arrays in superconducting niobium films as a function of geometry (size, shape, and density) and background magnetic field. By measuring the vortex expulsion field, we estimate the flux saturation number and flux trapping temperature for a range of geometries. We find that many moat designs effectively sequester flux in magnetically shielded environments (< 1 $μ$T), with high-aspect-ratio rectangular "slit" moats providing the strongest mitigation at minimal area cost. However, our measurements show that moats alone do not eliminate flux trapping in non-ideal films, as vortices can preferentially pin at material defects. These results provide design guidance for flux mitigation in superconducting integrated circuits and highlight the need for combined optimization of circuit geometries and materials.

cond-mat.supr-con↗

arXiv2026

Flux-trapping characterization for superconducting electronics using a cryogenic widefield N-$V$ diamond microscope

Magnetic flux trapping is a significant hurdle limiting the reliability and scalability of superconducting electronics, yet tools for imaging flux vortices remain slow or insensitive. We present a cryogenic widefield NV-diamond magnetic microscope capable of rapid, micrometer-scale imaging of flux trapping in superconducting devices. Using this technique, we measure vortex expulsion fields in Nb thin films and patterned strips, revealing a crossover in expulsion behavior between $10$ and $20~μ$m strip widths. The observed scaling agrees with theoretical models and suggests the influence of film defects on vortex expulsion dynamics. This instrument enables high-throughput magnetic characterization of superconducting materials and circuits, providing new insight for flux mitigation strategies in scalable superconducting electronics.

cond-mat.supr-con↗

arXiv2025

Machine Learning Enables Optimization of Diamond for Quantum Applications

Spins in solid-state materials, molecules, and other chemical systems have the potential to impact the fields of quantum sensing, communication, simulation, and computing. In particular, color centers in diamond, such as negatively charged nitrogen vacancy (NV$^-$) and silicon vacancy centers (SiV$^-$), are emerging as quantum platforms poised for transition to commercial devices. A key enabler stems from the semiconductor-like platform that can be tailored at the time of growth. The large growth parameter space makes it challenging to use intuition to optimize growth conditions for quantum performance. In this paper, we use supervised machine learning to train regression models using different synthesis parameters in over 100 quantum diamond samples. We train models to optimize NV$^-$ defects in diamond for high sensitivity magnetometry. Importantly, we utilize a magnetic-field sensitivity figure of merit (FOM) for NV magnetometry and use Bayesian optimization to identify critical growth parameters that lead to a 300% improvement over an average sample and a 55% improvement over the previous champion sample. Furthermore, using Shapley importance rankings, we gain new physical insights into the most impactful growth and post-processing parameters, namely electron irradiation dose, diamond seed depth relative to the plasma, seed miscut angle, and reactor nitrogen concentration. As various quantum devices can have significantly different material requirements, advanced growth techniques such as plasma-enhanced chemical vapor deposition (PE-CVD) can provide the ability to tailor material development specifically for quantum applications.

cond-mat.mtrl-sci↗