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Short Block-length Channel Coded Modulation with Random Linear Codes and QAOA Decoding
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Quantum approximate optimization algorithm (QAOA) is used for NP-hard
problems on noisy intermediate-scale quantum (NISQ) devices. We
demonstrate QAOA’s near-optimum maximum likelihood (ML) decoding for
short block-lengths in Gaussian channels using random linear codes and
channel coded modulation. Simulations with a p-layer QAOA decoder for
 p ∈ [1, 4], coding rates R = k/n ∈ [0.3, 1],
signal-to-noise ratio (SNR)  ∈  [0, 10] dB and k ∈ [10,
26] show near-optimum bit and block error rates. We conjecture
near-optimum performance for p ∈ [1, 10], R = 0.5, SNR = 10 dB and
k ≦ 250 indicating QAOA’s potential in short block-length decoding.
Title: Short Block-length Channel Coded Modulation with Random Linear Codes and QAOA Decoding
Description:
Quantum approximate optimization algorithm (QAOA) is used for NP-hard
problems on noisy intermediate-scale quantum (NISQ) devices.
We
demonstrate QAOA’s near-optimum maximum likelihood (ML) decoding for
short block-lengths in Gaussian channels using random linear codes and
channel coded modulation.
Simulations with a p-layer QAOA decoder for
 p ∈ [1, 4], coding rates R = k/n ∈ [0.
3, 1],
signal-to-noise ratio (SNR)  ∈  [0, 10] dB and k ∈ [10,
26] show near-optimum bit and block error rates.
We conjecture
near-optimum performance for p ∈ [1, 10], R = 0.
5, SNR = 10 dB and
k ≦ 250 indicating QAOA’s potential in short block-length decoding.
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