Welcome
I am Arie Soeteman, I am a PhD student working on Logical Reasoning for AI models at the University of Amsterdam, Institute for Logic, Language and Computation
About
My central interest lies in applying machine learning to solve logical problems and using logic to improve machine learning systems. I believe that by making AI more logical, we can build more generalizable, explainable and trustworthy systems.
My background is in Logic and Computation. From 2022 to 2024, I worked As a researcher and developer at Fermioniq, where I worked on quantum circuit emulation with tensor networks. I am currently pursuing a PhD under supervision of Balder ten Cate.
I enjoy teaching and presenting, and I’m always looking for ways to apply my technical background to socially meaningful problems. At the moment, I’m contributing to GOAL3, using AI and logic to predict critical illness events in low-resource intensive care units. The goal is to build an explainable prediction tool that can help save lives.
If you are interested in any of these topics and would like to discuss ideas, feel free to connect on Linkedin or send an email to a.w.[my surname]@uva.nl. I am always open to talk.
Selected Research
The logical expressiveness of graph neural networks with hierarchical node individualization
Arie Soeteman, Balder ten Cate
Under review
A known result on graph neural networks is that their separating power matches graded modal logic. We extend this result to subgraph GNNs, and give a new architecture with complete separating power, inspired by isomorphism testers. Paper Github
Non-zero noise extrapolation: accurately simulating noisy quantum circuits with tensor networks
Anthony Thompson, Arie Soeteman, Chris Cade, Ido Niesen
Under review
Running quantum computations on GPUs requires an exponential amount of resources. Tensor network methods like DMRG allow for efficient approximate simulation, but are unfit to match actual quantum hardware with noise. We develop non-zero noise extrapolation, and use this method to run noisy simulations previously beyond the reach of tensor networks. Paper
Spatial Unity for the Apperception Engine
Arie Soeteman, Michiel van Lambalgen
IJAR 2024
The Apperception Engine is a computational implementation of Kant's cognitive architecture from the first critique. The system constructs a logical theory that unifies sensory input. We use the Regional Connection Calculus and Alexandroff Topologies to build a Spatial Apperception Engine, following Kant's figurative synthesis. Paper Github
Participatory budgeting with multiple resources
Nima Motamed, Arie Soeteman, Simon Rey, Ulle Endriss
EUMAS 2022
In Participatory Budgeting, citizens vote on how to allocate public funds to projects—an approach shown to improve well-being and trust in government. The model is gaining global traction and is now used in my neighborhood in Amsterdam. We study a setting where projects require multiple resources and analyze how core properties like proportionality and strategy-proofness extend to this case. Paper
Science-related
Quantum circuit emulation with Ava
At Fermioniq, I was a core developer of Ava, a state-of-the-art tensor-network based quantum circuit emulator. Ava supports pure-state and noisy emulation via a simple python interface, with features like customizable noise models, circuit compression and quantum machine learning. If you want to explore how quantum computing could benefit your project, feel free to reach out to set up a conversation or experiment. Tutorial
Critical event prediction with GOAL3
GOAL3 is a social enterprise improving healthcare in low-resource settings. Its IMPALA system is a vital sign monitor used in Malawi, Rwanda and Tanzania that helps unburden health workers and supports better diagnosis and treatment. I am developing machine learning models with logical structure to predict critical events from IMPALA data. Let me know if you're interested or want to contribute.