2 citations · 3 across the 4 of their papers we have counts for
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Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks
Saud Čindrak, Lara Giebeler, Niclas Götting +2
Quantum reservoir computing (QRC) harnesses driven quantum dynamics for time-series processing, yet the mechanisms behind the differing performance levels across its many implement…
On Minimizing Krylov Complexity Using Higher-Order Generators
Saud Čindrak, Kathy Lüdge
Krylov complexity provides a powerful framework for characterizing the dynamical evolution of quantum systems through the spreading of states in Krylov space. The motivation for th…
From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
Saud Čindrak, Kathy Lüdge, Lina Jaurigue
We demonstrate that time-evolved operators can construct a Krylov space to compute Operator complexity and introduce Krylov observability as a measure of effective phase space dime…
Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
Saud Čindrak, Lina Jaurigue, Kathy Lüdge
This study employs Krylov-based information measures to understand task performance in quantum reservoir computing, a sub-field of quantum machine learning. In our study we show th…