16 citations · 78 across the 28 of their papers we have counts for
29 papers
Analytical Landscape of Maximal Magic for Two-Qutrit States and Beyond
Marco Knipfer, Alexander Roman, Katia Matcheva +1
Achieving a genuine quantum advantage relies on two distinct non-classical resources that restrict efficient classical simulation: entanglement and magic (nonstabilizerness). We in…
ASTER -- Agentic Science Toolkit for Exoplanet Research
Emilie Panek, Alexander Roman, Gaurav Shukla +3
The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing a…
The Pareto Frontiers of Magic and Entanglement: The Case of Two Qubits
Alexander Roman, Marco Knipfer, Jogi Suda Neto +3
Magic and entanglement are two measures that are widely used to characterize quantum resources. We study the interplay between magic and entanglement in two-qubit systems, focusing…
AI Agents for Variational Quantum Circuit Design
Marco Knipfer, Alexander Roman, Konstantin T. Matchev +2
Variational quantum circuits (VQCs) constitute a central building block of near-term quantum machine learning (QML), yet the principled design of expressive and trainable architect…
Hunting for "Oddballs" with Machine Learning: Detecting Anomalous Exoplanets Using a Deep-Learned Low-Dimensional Representation of Transit Spectra with Autoencoders
Alexander Roman, Emilie Panek, Roy T. Forestano +3
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures…
Balancing Variety and Sample Size: Optimal Parameter Sampling for Ariel Target Selection
Emilie Panek, Alexander Roman, Katia Matcheva +2
Targeted astrophysical surveys are limited by the amount of telescope time available, which makes it impossible to observe every single object of interest. In order to maximize the…
Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +1
Standard Bayesian retrievals for exoplanet atmospheric parameters from transmission spectroscopy, while well understood and widely used, are generally computationally expensive. In…
Quantum generative adversarial networks for gluon initiated jets generation
Rey Guadarrama, Sergei Gleyzer, Mariia Baidachna +6
Quantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics…
Quantum Diffusion Model for Quark and Gluon Jet Generation
Mariia Baidachna, Rey Guadarrama, Gopal Ramesh Dahale +6
Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel d…
Lie-Equivariant Quantum Graph Neural Networks
Jogi Suda Neto, Roy T. Forestano, Sergei Gleyzer +3
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquit…
Quantum Attention for Vision Transformers in High Energy Physics
Alessandro Tesi, Gopal Ramesh Dahale, Sergei Gleyzer +4
We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational effici…
Quantum Vision Transformers for Quark-Gluon Classification
Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong +8
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the mul…
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resource…
is all you need: production of multiple semi-invisible resonances at hadron colliders
Zhongtian Dong, Kyoungchul Kong, Konstantin T. Matchev +1
The stransverse mass variable was originally proposed for the study of hadron collider events in which parent particles are produced and then decay semi-invisibly. H…
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond +1
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the exist…
Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against thei…
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale +8
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from…
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond +1
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' crite…
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
Eyup B. Unlu, Roy T. Forestano, Konstantin T. Matchev +1
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of…
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone t…
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +1
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data…
Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry ge…
Discovering Sparse Representations of Lie Groups with Machine Learning
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
Recent work has used deep learning to derive symmetry transformations, which preserve conserved quantities, and to obtain the corresponding algebras of generators. In this letter,…
Oracle-Preserving Latent Flows
Alexander Roman, Roy T. Forestano, Konstantin T. Matchev +2
We develop a deep learning methodology for the simultaneous discovery of multiple nontrivial continuous symmetries across an entire labelled dataset. The symmetry transformations a…