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DY: Fachverband Dynamik und Statistische Physik
DY 49: Focus Session: Computing with Dynamical Systems: New Perspectives on Reservoirs and Applications II – Applications and Quantum RC
DY 49.8: Talk
Thursday, March 21, 2024, 17:30–17:45, BH-N 243
Exploring quantumness in quantum reservoir computing — •Niclas Götting1,2, Frederik Lohof1,2, and Christopher Gies1,2 — 1Institute for Theoretical Physics, University of Bremen, Bremen, Germany — 2Bremen Center for Computational Material Science, University of Bremen, Bremen, Germany
With the advent of sophisticated semiconductor fabrication techniques for quantum photonic systems like coupled-cavity arrays, Quantum Reservoir Computing becomes a promising candidate to elevate Reservoir Computing (RC) to the next level. Not only does the phase space dimension of the quantum system scale exponentially with its size, the property of quantum entanglement also introduces a new resource to RC.
We investigate how these properties are linked to the Quantum Reservoir Computer (QRC) performance in simple benchmarks [1]. As the noisy intermediate-scale quantum (NISQ) devices of our current time are subject to various types of perturbations, we also analyze how dephasing of the quantum state affects the benchmark performance.
[1] N. Götting et al. Exploring Quantumness in Quantum Reservoir Computing. Phys. Rev. A 2023, 108 (5), 052427.
Keywords: Quantum Reservoir Computing; Quantum Machine Learning; Quantum Entanglement