Marcin Płodzień

Characterizing out-of-distribution generalization of neural networks: application to the disordered Su–Schrieffer–Heeger model

2024 · Machine Learning Science and Technology

Abstract Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of what they learn from the data …

Many-body quantum resources of graph states

2025 · Reports on Progress in Physics

Abstract Characterizing the non-classical correlations of a complex many-body system is an important part of quantum technologies. An ideal tool for this task would scale well with the size of the system, be easily computable …

Non-stabilizerness in quantum-enhanced metrological protocols

2025 · arXiv (Cornell University)

Non-stabilizerness (colloquially "magic") characterizes genuinely quantum (beyond-Clifford) operations necessary for preparation of quantum states, and can be measured by stabilizer Rényi entropy (SRE). For permutationally symmetric states, we show that the SRE depends, for sufficiently …