SearcharxivSearch

arXiv subjects

Jaspal Singh

Publications and source records attributed to Jaspal Singh.

2 recordsLinked to original sources

Enhanced Magnon Synchronization in Coupled WGM Optomagnonic Resonators with Phase-Dependent Photon Hopping

We investigate quantum synchronization in a coupled cavity optomagnonic system which consists of two spatially separated optical whispering-gallery-mode (WGM) resonators and each resonator is also coupled to a yttrium iron garnet (YIG) sphere through the optomagnonic interaction. Phase-dependent single-photon hopping factor couples the two optical resonators and provides an indirect interaction between the two distant magnon modes. We then investigate complete synchronization, ϕ-synchronization, and quantum phase synchronization using the covariance-matrix formalism as well as also studying the effects of the hopping term on the overall synchronization dynamics of two distant magnon modes. It can be seen that the photon-hopping phase provides an efficient way to control the synchronization dynamics and when it is varied from 0 to π, the magnon trajectories gradually evolve from weakly correlated motion to a highly synchronized state, which is also accompanied by a significant reduction in the synchronization error. The influence of the photon-hopping strength and thermal fluctuations is also investigated, where it can be seen that stronger photon hopping enhances all synchronization measures, while thermal noise weakens the coherent correlations responsible for synchronized dynamics. Our results demonstrate that the phase of the hopping factor offers a simple and effective approach for controlling synchronization dynamics in WGM based coupled cavity optomagnonic systems and also provide a useful route towards coherent control of collective magnon dynamics in such quantum optomganonic devices.

quant-ph

Let's Ask Gauss: Improved One-Run Privacy Auditing

Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice. We study empirical privacy auditing for differentially private (DP) machine learning, focusing on efficient one-run methods for mechanisms such as DP-SGD. Prior one-run approaches threshold training examples or "canaries" into binary membership guesses, which discards useful information. We show that, in the white-box DP-SGD setting, canary-aligned signals naturally form a sequence of random variables whose normalized sum is asymptotically Gaussian. Leveraging this distributional perspective, we develop a DP-auditing framework that leads to tighter privacy lower bounds from a single training run.

cs.LG