Search Results
Fractal AI: A fragile theory of intelligence
Fractal AI is a theory for general artificial intelligence.
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties.
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
Design of quantum optical experiments with logic artificial intelligence
In this work, we propose the use of logic AI for the design of optical quantum experiments.
Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics
Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics.
A Quantum Natural Language Processing Approach to Musical Intelligence
In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars.
NCoder -- A Quantum Field Theory approach to encoding data
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder.
BILP-Q: Quantum Coalition Structure Generation
Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI.
