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Found 6 papers, 3 papers with code
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general.
Explainable Artificial Intelligence (XAI)
Quantum Machine Learning
Shapley Values with Uncertain Value Functions
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory.
On the effects of biased quantum random numbers on the initialization of artificial neural networks
Recent advances in practical quantum computing have led to a variety of cloud-based quantum computing platforms that allow researchers to evaluate their algorithms on noisy intermediate-scale quantum (NISQ) devices.
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
How can they be guaranteed for a certain implementation of a machine learning model?
Quantum Circuit Evolution on NISQ Devices
The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
