@philippaltmann@mas.to

· 7mo ago

RE: https://mastodon.social/@MunichQuantum/116159128026798506

This recent portrait reflects on my research at @MunichQuantum@mastodon.social, exploring alignment in collective intelligence across classical and quantum systems.

mastodon.social

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Philipp @philippaltmann@mas.to

· 42mo ago

Happy to share that our work on Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing (CROP) has been accepted for publication at #IJCAI2023! Looking forward to sharing our findings with the research community.

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Philipp @philippaltmann@mas.to

· 44mo ago

Happy to have presented our work on Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training (SEQUENT) at ICAART 2023. Check out the preprint here: https://arxiv.org/abs/2301.02601

arXiv.org

SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training

Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectu

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