arXiv subjects

Koray Aydoğan

Publications and source records attributed to Koray Aydoğan.

3 recordsLinked to original sources

arXiv2026

Subspace Selected Variational Quantum Configuration Interaction with a Partial Walsh Series

Estimating the ground-state energy of a quantum system is one of the most promising applications for quantum algorithms. Here we propose a variational quantum eigensolver (VQE) \emph{Ansatz} for finding ground state configuration interaction (CI) wavefunctions. We map CI for fermions to a quantum circuit using a subspace superposition, then apply diagonal Walsh operators to encode the wavefunction. The algorithm can be used to solve both full CI and selected CI wavefunctions, resuling in exact and near-exact solutions for electronic ground states. Both the subspace selection and wavefunction \emph{Ansatz} can be applied to any Hamiltonian that can be written in a qubit basis. The algorithm bypasses costly classical matrix diagonalizations, which is advantageous for large-scale applications. We demonstrate results for several molecules using quantum simulators and hardware.

quant-ph↗

arXiv2025

Stabilizing steady-state properties of open quantum systems with parameter engineering

Realistic quantum systems are affected by environmental loss, which is often seen as detrimental for applications in quantum technologies. Alternatively, weak coupling to an environment can aid in stabilizing highly entangled and mixed states, but determining optimal system-environment parameters can be challenging. Here, we describe a technique to optimize parameters for generating desired non-equilibrium steady states (NESSs) in driven-dissipative quantum systems governed by the Lindblad equation. We apply this approach to predict highly-entangled and mixed NESSs in Ising, Kitaev, and Dicke models in several quantum phases.

quant-ph↗

arXiv2022

Exoplanet Detection by Machine Learning with Data Augmentation

It has recently been demonstrated that deep learning has significant potential to automate parts of the exoplanet detection pipeline using light curve data from satellites such as Kepler \cite{borucki2010kepler} \cite{koch2010kepler} and NASA's Transiting Exoplanet Survey Satellite (TESS) \cite{ricker2010transiting}. Unfortunately, the smallness of the available datasets makes it difficult to realize the level of performance one expects from powerful network architectures. In this paper, we investigate the use of data augmentation techniques on light curve data from to train neural networks to identify exoplanets. The augmentation techniques used are of two classes: Simple (e.g. additive noise augmentation) and learning-based (e.g. first training a GAN \cite{goodfellow2020generative} to generate new examples). We demonstrate that data augmentation has a potential to improve model performance for the exoplanet detection problem, and recommend the use of augmentation based on generative models as more data becomes available.

astro-ph.EP↗