Parts | Days | Selection | Search | Updates | Downloads | Help
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 49.7: Invited Talk
Thursday, March 21, 2024, 17:00–17:30, BH-N 243
Opportunities in Quantum Reservoir Computing — •Roberta Zambrini — IFISC (UIB-CSIC)
Non-conventional computing inspired by the brain, (classical) neuromorphic computing, is a successful approach in a broad spectrum of applications, also burgeoning due to big data availability. Recent proposals go beyond classical substrates for quantum machine learning, as in Quantum Reservoir Computing. The classical version of this approach has been developed in the last 20 years and moving from classical to quantum physical reservoirs, has the potential to remarkably boost the processing performance in temporal tasks by exploiting quantum coherence, not requiring error correction. Furthermore this is naturally suited for fully quantum information processing (with quantum inputs). However, it also opens a series of new challenging questions, related to fundamental as well as implementation aspects. Examples are the identification of the best quantum regimes of operation, the role of statistics, or of quantum coherences and entanglement. After introducing the Quantum Reservoir Computing and showing how memory and non-linearity arise in a quantum formalism, we will discuss some of these issues, providing an overview of the field.
Keywords: neuromorphic computing; quantum machine learning; Quantum Reservoir Computing