arXiv subjects

Milo Coombs

Publications and source records attributed to Milo Coombs.

2 recordsLinked to original sources

arXiv2026

Understanding the superconducting proximity effect in semiconductors through quantum oscillations

Superconductor-semiconductor hybrids host emergent states of matter and offer a platform for new qubits, but the superconducting metal shunts electrical transport, which rules out conventional semiconductor characterization and leaves the hybrid parameters to speculation. Here we determine density, mass, $g$-factor, mobility and subband occupation beneath the superconductor, from Shubnikov-de Haas oscillations of a buried InAs quantum well under Al, Sn, V, Nb, Ta and Re films, with a Dingle analysis that accounts for the shunt. Every metal adds an interface subband whose occupation falls into one of two classes, whereas the mass and $g$-factor of the buried well are unchanged to within 10\%. Within the uncertainty set by the transport mobility, no film shortens the quantum lifetime of the buried well, and Al and Sn lengthen it. Quantum lifetimes bound the hybridization of the interface subband to 2-4~meV. These measurements supply the normal-state parameters that tunnelling spectroscopy renormalizes but cannot measure.

cond-mat.supr-con↗

arXiv2026

Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning

Classical approximation bases such as Chebyshev polynomials provide principled and interpretable representations, but their multivariate tensor-product constructions scale exponentially with dimension and impose axis-aligned structure that is poorly matched to real tabular data. We address this by replacing tensorised oscillations with directional harmonic modes of the form $\cos(\mathbf{m}^{\top}\arccos(\mathbf{x}))$, which organise multivariate structure by direction in angular space rather than by coordinate index. This representation yields a discrete spectral regression model in which complexity is controlled by selecting a small number of structured frequency vectors (spectral paths), and training reduces to a single closed-form ridge solve with no iterative optimisation. Experiments on standard continuous-feature tabular regression benchmarks show that the resulting models achieve accuracy competitive with strong nonlinear baselines while remaining compact, computationally efficient, and explicitly interpretable through analytic expressions of learned feature interactions.

cs.LG↗