6 papers
QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
Hoang-Quan Nguyen, Sankalp Pandey, Khoa Luu
Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through attention mechanisms, but their qua…
OpenQlaw: An Agentic AI Assistant for Analysis of 2D Quantum Materials
Sankalp Pandey, Xuan-Bac Nguyen, Hoang-Quan Nguyen +4
The transition from optical identification of 2D quantum materials to practical device fabrication requires dynamic reasoning beyond the detection accuracy. While recent domain-spe…
CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
Sankalp Pandey, Xuan Bac Nguyen, Nicholas Borys +2
Identifying quantum flakes is crucial for scalable quantum hardware; however, automated layer classification from optical microscopy remains challenging due to substantial appearan…
QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery
Xuan-Bac Nguyen, Hoang-Quan Nguyen, Sankalp Pandey +4
Characterizing two-dimensional quantum materials from optical microscopy images is challenging due to the subtle layer-dependent contrast, limited labeled data, and significant var…
QMoE: A Quantum Mixture of Experts Framework for Scalable Quantum Neural Networks
Hoang-Quan Nguyen, Xuan-Bac Nguyen, Sankalp Pandey +3
Quantum machine learning (QML) has emerged as a promising direction in the noisy intermediate-scale quantum (NISQ) era, offering computational and memory advantages by harnessing s…
-Adapt: A Physics-Informed Adaptation Learning Approach to 2D Quantum Material Discovery
Hoang-Quan Nguyen, Xuan Bac Nguyen, Sankalp Pandey +4
Characterizing quantum flakes is a critical step in quantum hardware engineering because the quality of these flakes directly influences qubit performance. Although computer vision…