mlsanityData to 14 Oct 2026

Learning to Learn Quantum Turbo Detection

arXiv 2205.08611 ↗First posted 17 May 2022eess.SP cs.ET cs.LGcited by 2

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Abstract (arXiv)

This paper investigates a turbo receiver employing a variational quantum circuit (VQC). The VQC is configured with an ansatz of the quantum approximate optimization algorithm (QAOA). We propose a 'learning to learn' (L2L) framework to optimize the turbo VQC decoder such that high fidelity soft-decision output is generated. Besides demonstrating the proposed algorithm's computational complexity, we show that the L2L VQC turbo decoder can achieve an excellent performance close to the optimal maximum-likelihood performance in a multiple-input multiple-output system.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 13 references that are in the map. First posted on arXiv 17 May 2022.

Builds on

2 of 13 references.

References · 2

  1. 2019Learning to learn with quantum neural networks via classical neural networkscited by 56
  2. 2018Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approachcited by 34

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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022quEEGNet: Quantum AI for Biosignal Processingcited by 1
  2. 2022Quantum Feature Extraction for THz Multi-Layer Imagingcited by 0