Search Results for author:
Found 4 papers, 1 papers with code
Quantum Embedding with Transformer for High-dimensional Data
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators.
Hybrid Quantum Neural Network in High-dimensional Data Classification
The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle.
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems
This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
