Search Results for author: Mario Krenn

Found 23 papers, 14 papers with code

Meta-Designing Quantum Experiments with Language Models

no code implementations4 Jun 2024 Sören Arlt, Haonan Duan, Felix Li, Sang Michael Xie, Yuhuai Wu, Mario Krenn

Artificial Intelligence (AI) has the potential to significantly advance scientific discovery by finding solutions beyond human capabilities.

Language Modelling

Generation and human-expert evaluation of interesting research ideas using knowledge graphs and large language models

no code implementations27 May 2024 Xuemei Gu, Mario Krenn

Advanced artificial intelligence (AI) systems with access to millions of research papers could inspire new research ideas that may not be conceived by humans alone.

Knowledge Graphs

Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics

1 code implementation20 Feb 2024 Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn

Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics.

Forecasting high-impact research topics via machine learning on evolving knowledge graphs

1 code implementation13 Feb 2024 Xuemei Gu, Mario Krenn

We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.

Knowledge Graphs

Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments

1 code implementation13 Sep 2023 Tareq Jaouni, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, Mario Krenn

Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement.

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library

1 code implementation7 Feb 2023 Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.

Artificial Intelligence and Machine Learning for Quantum Technologies

1 code implementation7 Aug 2022 Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly.

On scientific understanding with artificial intelligence

no code implementations4 Apr 2022 Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.


Design of quantum optical experiments with logic artificial intelligence

1 code implementation27 Sep 2021 Alba Cervera-Lierta, Mario Krenn, Alán Aspuru-Guzik

In this work, we propose the use of logic AI for the design of optical quantum experiments.

Formal Logic

Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models

1 code implementation6 Sep 2021 Daniel Flam-Shepherd, Tony Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alan Aspuru-Guzik

The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.

Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations

1 code implementation17 Dec 2020 Cynthia Shen, Mario Krenn, Sagi Eppel, Alan Aspuru-Guzik

We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.

BIG-bench Machine Learning

Scientific intuition inspired by machine learning generated hypotheses

no code implementations27 Oct 2020 Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alan Aspuru-Guzik

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas.

BIG-bench Machine Learning

Quantum Computer-Aided design of Quantum Optics Hardware

2 code implementations4 Jun 2020 Jakob S. Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, Alán Aspuru-Guzik

It is not clear how the full potential of large quantum systems can be exploited.

Quantum Physics Computational Physics Optics

Conceptual understanding through efficient inverse-design of quantum optical experiments

no code implementations13 May 2020 Mario Krenn, Jakob Kottmann, Nora Tischler, Alán Aspuru-Guzik

Here we present Theseus, an efficient algorithm for the design of quantum experiments, which we use to solve several open questions in experimental quantum optics.

Quantum Physics Optics

Computer-inspired Quantum Experiments

no code implementations23 Feb 2020 Mario Krenn, Manuel Erhard, Anton Zeilinger

We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future.

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics

1 code implementation17 Jun 2019 Mario Krenn, Anton Zeilinger

We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data.

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation

2 code implementations31 May 2019 Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.

molecular representation valid

Active learning machine learns to create new quantum experiments

no code implementations2 Jun 2017 Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel

We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.

Active Learning

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