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Abstract
Quantum extreme learning machines (QELMs) aim to efficiently post-process the outcome of fixed quantum devices to solve tasks such as estimating quantum state properties. This paper presents a framework to model QELMs, showing they can be described via single effective measurements, and characterizing retrievable information. The training process is analogous to reconstructing the effective measurement, paving the way for understanding QELM capabilities and limitations, potentially creating a more noise-resilient measurement paradigm for quantum state estimation.
Publisher
COMMUNICATIONS PHYSICS
Published On
May 25, 2023
Authors
L. Innocenti, S. Lorenzo, I. Palmisano, A. Ferraro, M. Paternostro, G. M. Palma
Tags
Quantum Extreme Learning Machines
quantum state estimation
effective measurements
data processing
noise resilience

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