9 citations · 28 across the 17 of their papers we have counts for
18 papers
Efficient Hamiltonian Truncation: Fast Matrix Construction and Quantum Krylov Diagonalization
Rachel Houtz, Marco Knipfer, Konstantin Matchev +2
Hamiltonian truncation offers a nonperturbative route to quantum field theory, yet its accuracy is limited by the rapid expansion of the truncated Hilbert space, which drives up co…
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…
Agentic Diagrammatica: Towards Autonomous Symbolic Computation in High Energy Physics
Tony Menzo, Alexander Roman, George T. Fleming +3
We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symb…
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…
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…
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…
Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles
Roy T. Forestano, Konstantin T. Matchev, Katia Matcheva +3
We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks…
Transverse Vector Decomposition Method for Analytical Inversion of Exoplanet Transit Spectra
Konstantin T. Matchev, Katia Matcheva, Alexander Roman
We develop a new method for analytical inversion of binned exoplanet transit spectra and for retrieval of planet parameters. The method has a geometrical interpretation and treats…
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra
Konstantin T. Matchev, Katia Matcheva, Alexander Roman
Transit spectroscopy is a powerful tool to decode the chemical composition of the atmospheres of extrasolar planets. In this paper we focus on unsupervised techniques for analyzing…
Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression
Konstantin T. Matchev, Katia Matcheva, Alexander Roman
The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra which are obtained from complex numer…
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in…
Uncertainties associated with GAN-generated datasets in high energy physics
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets a…