2 citations · 5 across the 9 of their papers we have counts for
9 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…
Universal Quantum Suppression in Frustrated Ising Magnets across the Quasi-1D to 2D Crossover via Quantum Annealing
Kumar Ghosh
Quantum magnets in the and BaCoVO families realise frustrated transverse-field Ising models whose competing ferromagnetic and antiferromagnetic coup…
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…
Quantum simulation of interlayer charge ordering in Kagome frustrated-magnet
Kumar Ghosh, Sabre Kais
The interplay between interlayer coupling and quantum fluctuations governs charge ordering and defect dynamics in Kagome systems, yet these parameters are intrinsically entangled i…
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Fidelity quantum kernels have shown promise in classification tasks, particularly when a group structure in the data can be identified and exploited through a covariant feature map…
Quantum multi-output Gaussian Processes based Machine Learning for Line Parameter Estimation in Electrical Grids
Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara +3
Gaussian process (GP) is a powerful modeling method with applications in machine learning for various engineering and non-engineering fields. Despite numerous benefits of modeling…
Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games
Jonas Nüßlein, Daniëlle Schuman, David Bucher +5
The transition to 100% renewable energy requires new techniques for managing energy networks, such as dividing them into sensible subsets of prosumers called micro-grids. Doing so…
A Machine Learning Approach to Boost the Vehicle-2-Grid Scheduling
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar +6
Electric Vehicles (EVs) are emerging as battery energy storage systems (BESSs) of increasing importance for different power grid services. However, the unique characteristics of EV…
Bridging the Gap to Next Generation Power System Planning and Operation with Quantum Computation
Priyanka Arkalgud Ganeshamurthy, Kumar Ghosh, Corey O'Meara +2
Innovative solutions and developments are being inspected to tackle rising electrical power demand to be supplied by clean forms of energy. The integration of renewable energy gene…