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Found 4 papers, 1 papers with code
Sub-universal variational circuits for combinatorial optimization problems
Quantum variational circuits have gained significant attention due to their applications in the quantum approximate optimization algorithm and quantum machine learning research.
On the Interpretability of Quantum Neural Networks
Here, we explore the interpretability of quantum neural networks using local model-agnostic interpretability measures commonly utilized for classical neural networks.
An Invitation to Distributed Quantum Neural Networks
In this review, we consider ideas from distributed deep learning as they apply to quantum neural networks.
Quantum Optimization for Training Quantum Neural Networks
Training quantum neural networks (QNNs) using gradient-based or gradient-free classical optimisation approaches is severely impacted by the presence of barren plateaus in the cost landscapes.
