Mila Bileska

activity

20242026

^{171}

Yb Rydberg states for high-fidelity two-qubit gates

1 citations · 1 across the 2 of their papers we have counts for

collaborators

3 papers

hep-ex2026

Reconfigurable On-Chip AI for Particle Physics Detectors with Logic Neural Networks

Mila Bileska, Lino Gerlach, Jennet Dickinson +4

On-device machine learning is increasingly important in applications where extreme data rates or resource constraints make centralized processing infeasible. Logic neural networks…

physics.ins-det2025

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted $H \to b b ˉ$ Tagging at the CMS Experiment

Mila Bileska

This thesis investigates the physics performance, trigger efficiency, and Field Programmable Gate Array (FPGA) implementation of machine learning (ML)-based algorithms for Lorentz-…

physics.atom-ph2024★ 1 cited

Spectroscopy and modeling of $^{171}$ Yb Rydberg states for high-fidelity two-qubit gates

Michael Peper, Yiyi Li, Daniel Y. Knapp +8

Highly excited Rydberg states and their interactions play an important role in quantum computing and simulation. These properties can be predicted accurately for alkali atoms with…

Reconfigurable On-Chip AI for Particle Physics Detectors with Logic Neural Networks

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted H→bbˉ Tagging at the CMS Experiment

Spectroscopy and modeling of 171Yb Rydberg states for high-fidelity two-qubit gates

Design and FPGA Implementation of WOMBAT: A Deep Neural Network Level-1 Trigger System for Jet Substructure Identification and Boosted $H \to b b ˉ$ Tagging at the CMS Experiment

Spectroscopy and modeling of $^{171}$ Yb Rydberg states for high-fidelity two-qubit gates