49 citations · 117 across the 15 of their papers we have counts for
21 papers
Quantum Graph Transformer for NLP Sentiment Classification
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critica…
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bärtschi, Diane Oyen +2
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs…
LLVM Static Analysis for Program Characterization and Memory Reuse Profile Estimation
Atanu Barai, Nandakishore Santhi, Abdur Razzak +2
Profiling various application characteristics, including the number of different arithmetic operations performed, memory footprint, etc., dynamically is time- and space-consuming.…
Predicting Expressibility of Parameterized Quantum Circuits using Graph Neural Network
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +2
Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a…
Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection
Amin Sarihi, Ahmad Patooghy, Peter Jamieson +1
Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmark circuits are generated with multiple shortcomi…
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach
Amin Sarihi, Peter Jamieson, Ahmad Patooghy +1
Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserte…