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20212026
most citedHybrid Classical-Quantum Autoencoder for Anomaly Detection

47 citations · 108 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy +19

Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict…

quant-ph2022★ 7 cited

Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

Raúl Berganza Gómez, Corey O'Meara, Giorgio Cortiana +2

The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent y…

quant-ph2021★ 47 cited

Hybrid Classical-Quantum Autoencoder for Anomaly Detection

Alona Sakhnenko, Corey O'Meara, Kumar J. B. Ghosh +3

We propose a Hybrid classical-quantum Autoencoder (HAE) model, which is a synergy of a classical autoencoder (AE) and a parametrized quantum circuit (PQC) that is inserted into its…

quant-ph2021★ 14 cited

Community Detection in Electrical Grids Using Quantum Annealing

Marina Fernández-Campoamor, Corey O'Meara, Giorgio Cortiana +2

With the increase of intermittent renewable generation resources feeding into the electrical grid, Distribution System Operators (DSOs) must find ways to incorporate these new acto…

quant-ph2021★ 40 cited

Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification

Stephen DiAdamo, Corey O'Meara, Giorgio Cortiana +1

In this work, we aim to solve a practical use-case of unsupervised clustering which has applications in predictive maintenance in the energy operations sector using quantum compute…