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Found 14 papers, 8 papers with code
An Optimization Case Study for solving a Transport Robot Scheduling Problem on Quantum-Hybrid and Quantum-Inspired Hardware
We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-the-art classical solver in solving a transport robot scheduling problem.
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.
Representation of binary classification trees with binary features by quantum circuits
We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach.
Wavelet-Packets for Deepfake Image Analysis and Detection
Additionally, this paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and GAN-generated images.
Calibrated simplex-mapping classification
In a second step, the latent space representation of the training data is extended to the whole feature space by fitting a regression model to the transformed data.
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.
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification
We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization.
CupNet -- Pruning a network for geometric data
Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network in a straightforward way.
The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge
We address a non-unique parameter fitting problem in the context of material science.
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
It considers the source of knowledge, its representation, and its integration into the machine learning pipeline.
Optimized data exploration applied to the simulation of a chemical process
We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way.
