7 citations · 15 across the 5 of their papers we have counts for
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GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks
Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +2
Hybrid Quantum Neural Networks (HQNNs) combine classical learning with parameterized quantum circuits, but their practical performance is often limited by (i) the noise of Noisy In…
Noisy HQNNs: A Comprehensive Analysis of Noise Robustness in Hybrid Quantum Neural Networks
Tasnim Ahmed, Alberto Marchisio, Muhammad Kashif +1
Hybrid Quantum Neural Networks (HQNNs) offer promising potential of quantum computing while retaining the flexibility of classical deep learning. However, the limitations of Noisy…
Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation
Tasnim Ahmed, Muhammad Kashif, Alberto Marchisio +1
In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning…
Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks
Kamila Zaman, Tasnim Ahmed, Muhammad Kashif +3
In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural networks (HQNNs) represent a promising solution that combines the strengths of classical machin…
A Comparative Analysis of Hybrid-Quantum Classical Neural Networks
Kamila Zaman, Tasnim Ahmed, Muhammad Abdullah Hanif +2
Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current no…