9 citations · 28 across the 17 of their papers we have counts for
8 papers · 1 filter
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…