"Quantum Reservoir Computing on Random Regular Graphs" preprint paper released

Moein Ivaki, Achilleas Lazarides, and Tapio Ala-Nissila have now released a preprint of their paper "Quantum Reservoir Computing on Random Regular Graphs" 📄: https://arxiv.org/abs/2409.03665

Quantum Reservoir Computing (QRC) is a novel approach that utilises the complex dynamics of many-body quantum systems, combined with classical learning techniques, to process temporal data. They explore the QRC process by introducing an interacting spin model on random regular graphs, focusing on how disorder, interactions, and network connectivity influence learning efficiency.

They find that these factors critically impact the system's memory and learning capabilities. Notably, they discover that quantum correlations and network structure play a significant role in enhancing the performance of disordered quantum reservoirs. Their findings provide a blueprint for optimising the performance of analogue quantum learning systems.

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