Soft Reverse Reconciliation for Discrete Modulations

• URL: http://arxiv.org/abs/2411.04063v1
• Date: Wed, 06 Nov 2024 17:28:28 GMT
• Title: Soft Reverse Reconciliation for Discrete Modulations
• Authors: Marco Origlia, Marco Secondini,
• Abstract summary: This work introduces a reverse reconciliation softening (RRS) procedure designed for CV-QKD scenarios employing discrete modulations. We investigate how the mutual information between Alice's and Bob's variables changes when the additional metric is shared. We show numerically that RRS improves the mutual information with respect to RR with hard decoding, practically achieving the same mutual information as DR with soft decoding.
• Score: 0.552480439325792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of the information reconciliation phase is crucial for quantum key distribution (QKD). Reverse reconciliation (RR) is typically preferred over direct reconciliation (DR) because it yields higher secure key rates. However, a significant challenge in continuous-variable (CV) QKD with discrete modulations (such as QAM) is that Alice lacks soft information about the symbol decisions made by Bob. This limitation restricts error correction to hard-decoding methods, with low reconciliation efficiency. This work introduces a reverse reconciliation softening (RRS) procedure designed for CV-QKD scenarios employing discrete modulations. This procedure generates a soft metric that Bob can share with Alice over a public channel, enabling her to perform soft-decoding error correction without disclosing any information to a potential eavesdropper. After detailing the RRS procedure, we investigate how the mutual information between Alice's and Bob's variables changes when the additional metric is shared. We show numerically that RRS improves the mutual information with respect to RR with hard decoding, practically achieving the same mutual information as DR with soft decoding. Finally, we test the proposed RRS for PAM-4 signalling with a rate 1/2 binary LDPC code and bit-wise decoding through numerical simulations, obtaining more than 1dB SNR improvement compared to hard-decoding RR.

Related papers

• Residual Context Diffusion Language Models [90.07635240595926]
  Residual Context Diffusion (RCD) is a module that converts discarded token representations into contextual residuals and injects them back for the next denoising step.<n>RCD consistently improves frontier dLLMs by 5-10 points in accuracy with minimal extra computation overhead.
  arXiv  Detail & Related papers  (2026-01-30T13:16:32Z)
• Increased-Efficiency Multiple-Decoding-Attempts Error Correction for Continuous-Variable Quantum Key Distribution [0.0]
  We show how to improve on the recently introduced implementation of an IR-protocol involving multiple decoding attempts.<n>We demonstrate meaningful SKR-gains compared to both the standard protocol of a single decoding attempt.
  arXiv  Detail & Related papers  (2025-12-30T18:02:04Z)
• Random coding for long-range continuous-variable QKD [0.18907108368038208]
  Quantum Key Distribution (QKD) schemes are key exchange protocols based on the physical properties of quantum channels.<n>In this paper we introduce a random-codebook error correction method that is suitable for long range CVQKD.
  arXiv  Detail & Related papers  (2025-12-17T21:45:59Z)
• Joint parameter estimation and multidimensional reconciliation for CV-QKD [7.277058557395869]
  We propose a novel joint message-passing scheme that unifies channel parameter estimation and information reconciliation within a Bayesian framework.<n>To the best of our knowledge, this is the first work to unify multidimensional reconciliation and channel parameter estimation in CV-QKD.
  arXiv  Detail & Related papers  (2025-08-07T16:38:33Z)
• R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning [60.37610817226533]
  Chain-of-thought (CoT) reasoning encourages step-by-step intermediate reasoning during inference.<n>CoT introduces substantial computational overhead due to its reliance on autoregressive decoding over long token sequences.<n>We present R-Stitch, a token-level, confidence-based hybrid decoding framework that accelerates CoT inference.
  arXiv  Detail & Related papers  (2025-07-23T08:14:36Z)
• Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary Codes [84.0257274213152]
  Iterative bit flipping decoders are an efficient choice for sparse $(v,w)$-regular codes. We propose concrete criteria for threshold determination, backed by a closed form model.
  arXiv  Detail & Related papers  (2025-01-23T17:38:22Z)
• Exact Calculations of Coherent Information for Toric Codes under Decoherence: Identifying the Fundamental Error Threshold [0.0]
  The toric code is a canonical example of a topological error-correcting code. Recent studies have explored such a threshold behavior as an intrinsic information-theoretic transition. We present the first analytic expression for the coherent information of a decohered toric code.
  arXiv  Detail & Related papers  (2024-02-26T19:00:00Z)
• Asymmetric adaptive LDPC-based information reconciliation for industrial quantum key distribution [0.0]
  We develop a new approach for asymmetric LDPC-based information reconciliation in order to adapt to the current channel state. The new scheme combines the advantages of LDPC codes, a priori error rate estimation, rate-adaptive and blind information reconciliation techniques.
  arXiv  Detail & Related papers  (2022-12-02T12:09:09Z)
• ASR Error Correction with Constrained Decoding on Operation Prediction [8.701142327932484]
  We propose an ASR error correction method utilizing the predictions of correction operations. Experiments on three public datasets demonstrate the effectiveness of the proposed approach in reducing the latency of the decoding process.
  arXiv  Detail & Related papers  (2022-08-09T09:59:30Z)
• Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks [26.312293813063558]
  We develop a novel adaptive codebook-based limited feedback protocol to control the intelligent reflecting surfaces (IRS) We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC) Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.
  arXiv  Detail & Related papers  (2022-05-07T11:21:19Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• SreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm [60.61943386819384]
  Existing implementations of KRR require that all the data is stored in the main memory. We propose StreaMRAK - a streaming version of KRR. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum.
  arXiv  Detail & Related papers  (2021-08-23T21:03:09Z)
• Performance of teleportation-based error correction circuits for bosonic codes with noisy measurements [58.720142291102135]
  We analyze the error-correction capabilities of rotation-symmetric codes using a teleportation-based error-correction circuit. We find that with the currently achievable measurement efficiencies in microwave optics, bosonic rotation codes undergo a substantial decrease in their break-even potential.
  arXiv  Detail & Related papers  (2021-08-02T16:12:13Z)
• Neural Distributed Source Coding [59.630059301226474]
  We present a framework for lossy DSC that is agnostic to the correlation structure and can scale to high dimensions. We evaluate our method on multiple datasets and show that our method can handle complex correlations and state-of-the-art PSNR.
  arXiv  Detail & Related papers  (2021-06-05T04:50:43Z)
• Robust Reference-based Super-Resolution via C2-Matching [77.51610726936657]
  Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. We propose C2-Matching, which produces explicit robust matching crossing transformation and resolution.
  arXiv  Detail & Related papers  (2021-06-03T16:40:36Z)
• Composably secure data processing for Gaussian-modulated continuous variable quantum key distribution [58.720142291102135]
  Continuous-variable quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties. We consider a protocol with homodyne detection in the general setting of composable finite-size security. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes.
  arXiv  Detail & Related papers  (2021-03-30T18:02:55Z)
• Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip [41.28049430114734]
  We propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. Our IABF can achieve state-of-the-art performances on both compression and error correction benchmarks and outperform the baselines by a significant margin.
  arXiv  Detail & Related papers  (2020-04-03T10:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.