5 papers · 1 filter
Efficient time-series prediction on NISQ devices via time-delayed quantum extreme learning machine
Mio Kawanabe, Saud Cindrak, Kathy Ludge +3
We proposed a time-delayed quantum extreme learning machine (TD-QELM) for efficient time-series prediction on noisy intermediate-scale quantum (NISQ) devices. By encoding multiple…
Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks
Saud Äindrak, 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 Äindrak, 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 Äindrak, 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 Äindrak, 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…