5 citations · 11 across the 16 of their papers we have counts for
17 papers
QGrAT: Quantum Grade-Aware Transformer for Jet Tagging
Pallavi Bhardwaj, Michael Spannowsky
Lorentz symmetry and the three-prong structure of hadronic top decays provide physical guidance for designing compact quantum jet taggers. We introduce QGrAT (Quantum Grade-Aware T…
Equivalence of maximal and generic reachability for non-universal Variational Quantum Circuits
Vishal S. Ngairangbam, Michael Spannowsky
Employing problem-specific non-universal Variational Quantum Circuits aligned with a suitable state preparation has become a standard approach to counter the difficulty in training…
Backdoors in Learning-Based Industrial Robotic Arm Manipulation: An Empirical Security Study
Zijian Zhang, Zhen Zeng, Zhongshu Gu +1
Learning-based models (e.g., visuomotor and Vision-Language-Action (VLA)) are increasingly explored for industrial robotic manipulation, where model predictions are directly transl…
Qubit-qubit-qutrit quantum correlations in
Michał Banacki, Misaki Ohta, Abhyoudai S. Shaleena +5
We perform an extensive analysis of the quantum correlations carried by the qubit-qubit-qutrit pure state arising in the decay of a massive scalar into a fermion-antifermion pair a…
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jack Y. Araz, Michael Spannowsky
Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a mod…
QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models
Aritra Bal, Michael Binder, Markus Klute +2
Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (QUantum-Informed Views for En…
From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs
Aritra Bal, Markus Klute, Benedikt Maier +1
Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise co…
Searching for axions with quantum interferometry
Tanmay Kumar Poddar, Michael Spannowsky
Quantum phase measurements offer a complementary route to axion searches. We show that axion-photon interactions can imprint both Aharonov-Bohm (AB) and Berry phases in experimenta…
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
Emre Gurkanli, Michael Spannowsky
We investigate whether a multiscale tensor-network architecture can provide a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced b…
From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks
Vishal S. Ngairangbam, Michael Spannowsky
Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reach…
Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
Partha Konar, Vishal S. Ngairangbam, Michael Spannowsky +1
Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known…
Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision proc…
Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
Vishal S. Ngairangbam, Michael Spannowsky, Timur Sypchenko
We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum ma…
Enhancing anomaly detection with topology-aware autoencoders
Vishal S. Ngairangbam, Błażej Rozwoda, Kazuki Sakurai +1
Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the…
Three-Body Non-Locality in Particle Decays
Paweł Horodecki, Kazuki Sakurai, Abhyoudai S. Shaleena +1
The exploration of entanglement and Bell non-locality among multi-particle quantum systems offers a profound avenue for testing and understanding the limits of quantum mechanics an…
Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods
Daniel Maître, Vishal S. Ngairangbam, Michael Spannowsky
The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to…
Collective variables of neural networks: empirical time evolution and scaling laws
Samuel Tovey, Sven Krippendorf, Michael Spannowsky +2
This work presents a novel means for understanding learning dynamics and scaling relations in neural networks. We show that certain measures on the spectrum of the empirical neural…