logo
ResearchBunny Logo
Potential and limitations of quantum extreme learning machines

Physics

Potential and limitations of quantum extreme learning machines

L. Innocenti, S. Lorenzo, et al.

Discover how Quantum Extreme Learning Machines (QELMs) can transform quantum state estimation by leveraging single effective measurements. This innovative framework, developed by L. Innocenti, S. Lorenzo, I. Palmisano, A. Ferraro, M. Paternostro, and G. M. Palma, paves the way for noise-resilient measurement paradigms.

00:00
00:00
Playback language: English
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

Related Publications

Explore these studies to deepen your understanding of the subject.

TRANSLATING RADIOLOGY REPORTS INTO PLAIN LANGUAGE USING CHATGPT AND GPT-4 WITH PROMPT LEARNING: PROMISING RESULTS, LIMITATIONS, AND POTENTIAL
Medicine and Health

TRANSLATING RADIOLOGY REPORTS INTO PLAIN LANGUAGE USING CHATGPT AND GPT-4 WITH PROMPT LEARNING: PROMISING RESULTS, LIMITATIONS, AND POTENTIAL

Q. Lyu, J. Tan, et al.

The Potential and Limitations of Large Language Models for Text Classification through Synthetic Data Generation
Computer Science

The Potential and Limitations of Large Language Models for Text Classification through Synthetic Data Generation

A. K. P. Venkata and L. Gudala

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter
Biology

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter

A. Hoarfrost, A. Aptekmann, et al.

The examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement
Education

The examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement

H. Lei, C. Chen, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny