12 papers
Hybrid Quantum-Classical Machine Learning Algorithms for Multi-Output Time-Series Forecasting at Utility Scale
Mackenson Polché, Varun Puram, Aditi Lal +6
Multi-output time-series forecasting in energy systems is challenging because of nonlinear dynamics, multi-scale seasonality, and strong dependencies across correlated series. In t…
Breaking concentration barriers for quantum extreme learning on digital quantum processors
Timothée Dao, Ege Yilmaz, Ibrahim Shehzad +8
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their pra…
Constrained Quantum Optimization at Utility Scale: Application to the Knapsack Problem
Naeimeh Mohseni, Julien-Pierre Houle, Ibrahim Shehzad +3
Constrained combinatorial optimization problems are challenging for quantum computing, particularly at utility-relevant scales and on near-term hardware. At the same time, these pr…
Boosting Sparsity in Graph Decompositions with QAOA Sampling
George Pennington, Naeimeh Mohseni, Oscar Wallis +5
We study the problem of decomposing a graph into a weighted sum of a small number of matchings, a task that arises in network resource allocation problems such as peer-to-peer ener…
Quantum Computing in the Computational Landscape of Power Electronics: Vision and Reality
Nikolaos G. Paterakis, Petros Karamanakos, Corey O'Meara +1
Quantum computing is rapidly emerging as a promising technology for solving complex optimization problems that arise in various engineering fields. Therefore, it holds significant…
A Lie Theoretic Framework for Controlling Open Quantum Systems
Corey O'Meara
This thesis focuses on the Lie-theoretic foundations of controlled open quantum systems. We describe Markovian open quantum system evolutions by Lie semigroups, whose corresponding…