SearcharxivSearch

arXiv subjects

Roy Leibovitz

Publications and source records attributed to Roy Leibovitz.

2 recordsLinked to original sources

Probing the Dynamics of Two-Level System Defect Ensembles via Broadband Cryogenic Transient Dielectric Spectroscopy

Two-level system (TLS) defects in dielectrics are a major source of decoherence in superconducting circuits, yet their microscopic origin and distribution remain poorly understood. Existing circuit-QED probes access limited frequency ranges and mode volumes, restricting studies of isolated materials and interfaces. Here, we present Broadband Cryogenic Transient Dielectric Spectroscopy (BCTDS), a technique for probing TLS-hosting materials over a broad frequency range at cryogenic temperatures. Under strong finite-duration microwave excitation, the transient homodyne I-Q response exhibits coherent phase dynamics after the drive is turned off. Fourier analysis of the transient phase reveals characteristic V-shaped structures that move between cooldowns, consistent with thermocycling-induced changes in the local TLS defect environment that shift defect resonance frequencies. The transient response of BCTDS further enables estimation of susceptibility and two-time correlation functions of the TLS defect ensemble. The observed phase dynamics are qualitatively captured by a driven standard tunneling model containing only a few representative TLS defects. Despite its simplicity relative to the full experimental ensemble, the model reproduces the essential Floquet-dressed dynamics during the drive and generates post-pulse V-shaped structures and interference fringes consistent with the experimental data. The observed BCTDS response may reflect a crossover from localized TLS defect dynamics to a delocalized regime under strong driving, before being quenched into a transient regime that reflects the TLS defect resonance frequencies. Overall, BCTDS represents a potentially useful broadband, time-resolved wafer-level approach for probing TLS defects relevant to quantum technologies.

quant-ph

ZipNN: Lossless Compression for AI Models

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression. We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly, we show that specific lossless compression can gain significant network and storage reduction on popular models, often saving 33% and at times reducing over 50% of the model size. We investigate the source of model compressibility and introduce specialized compression variants tailored for models that further increase the effectiveness of compression. On popular models (e.g. Llama 3) ZipNN shows space savings that are over 17% better than vanilla compression while also improving compression and decompression speeds by 62%. We estimate that these methods could save over an ExaByte per month of network traffic downloaded from a large model hub like Hugging Face.

cs.LG