Fractal AI is a theory for general artificial intelligence.
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.
Yet, applying generative AI techniques to accelerate the design of analog ICs remains a significant challenge due to critical domain-specific issues, such as the lack of a comprehensive dataset and effective representation methods for analog circuits.
We introduce a prototyping testbed, GenSC-6G, developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth-generation (6G) applications.
Quantum gates constitute the building blocks of gate-based quantum hardware and form circuits that can be used for quantum computations.
Explainable Artificial Intelligence (XAI)
Quantum Machine Learning
Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics.
In particular, we are importing methods from the Distributional Compositional Categorical (DisCoCat) modelling framework for Natural Language Processing (NLP), motivated by musical grammars.
In this paper we present a novel approach to interpretable AI inspired by Quantum Field Theory (QFT) which we call the NCoder.