286 citations · 290 across the 5 of their papers we have counts for
5 papers
QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +2
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuit…
Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks
Renyang Liu, Wei Zhou, Jinhong Zhang +3
Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in…
Quantum computing for finance
Dylan Herman, Cody Googin, Xiaoyuan Liu +5
Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehe…
Similarity-Based Parameter Transferability in the Quantum Approximate Optimization Algorithm
Alexey Galda, Eesh Gupta, Jose Falla +4
The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. A…
Learning To Optimize Quantum Neural Network Without Gradients
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +1
Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This exte…