6 papers · 1 filter
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…
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 NüÃlein, 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…
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…