Computer-inspired quantum experiments
The design of new devices and experiments has historically relied on the intuition of human
experts. Now, design inspirations from computers are increasingly augmenting the capability …
experts. Now, design inspirations from computers are increasingly augmenting the capability …
Experimental quantum speed-up in reinforcement learning agents
As the field of artificial intelligence advances, the demand for algorithms that can learn
quickly and efficiently increases. An important paradigm within artificial intelligence is …
quickly and efficiently increases. An important paradigm within artificial intelligence is …
Learning models of quantum systems from experiments
As Hamiltonian models underpin the study and analysis of physical and chemical
processes, it is crucial that they are faithful to the system they represent. However …
processes, it is crucial that they are faithful to the system they represent. However …
Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
Shortening quantum circuits is crucial to reducing the destructive effect of environmental
decoherence and enabling useful algorithms. Here, we demonstrate an improvement in …
decoherence and enabling useful algorithms. Here, we demonstrate an improvement in …
Automated gadget discovery in the quantum domain
LM Trenkwalder, A López-Incera… - Machine Learning …, 2023 - iopscience.iop.org
In recent years, reinforcement learning (RL) has become increasingly successful in its
application to the quantum domain and the process of scientific discovery in general …
application to the quantum domain and the process of scientific discovery in general …
Supervised graph classification for chiral quantum walks
Particle transport in quantum systems, which can be modeled by quantum walks on graphs,
demonstrates a faster propagation advantage over the corresponding transport in classical …
demonstrates a faster propagation advantage over the corresponding transport in classical …
Learning minimal representations of stochastic processes with variational autoencoders
Stochastic processes have found numerous applications in science, as they are broadly
used to model a variety of natural phenomena. Due to their intrinsic randomness and …
used to model a variety of natural phenomena. Due to their intrinsic randomness and …
Forming complex neurons by four-wave mixing in a Bose-Einstein condensate
KN Hansmann, R Walser - Physical Review A, 2024 - APS
A physical artificial complex-valued neuron is formed by four-wave mixing in a
homogeneous three-dimensional Bose-Einstein condensate. Bragg beam-splitter pulses …
homogeneous three-dimensional Bose-Einstein condensate. Bragg beam-splitter pulses …
Explainable representation learning of small quantum states
Unsupervised machine learning models build an internal representation of their training
data without the need for explicit human guidance or feature engineering. This learned …
data without the need for explicit human guidance or feature engineering. This learned …
Introducing a four-fold way to conceptualize artificial agency
M van Lier - Synthese, 2023 - Springer
Recent developments in AI-research suggest that an AI-driven science might not be that far
off. The research of for Melnikov et al.(2018) and that of Evans et al.(2018) show that …
off. The research of for Melnikov et al.(2018) and that of Evans et al.(2018) show that …