Gaussian benchmark for optical communication aiming towards ultimate capacity

Abstract

We establish the fundamental limit of communication capacity within Gaussian schemes under phase-insensitive Gaussian channels, which employ multimode Gaussian states for encoding and collective Gaussian operations and measurements for decoding. We prove that this Gaussian capacity is additive, i.e., its upper bound occurs with separable encoding and separable receivers so that a single-mode communication suffices to achieve the largest capacity under Gaussian schemes. This rigorously characterizes the gap between the ultimate Holevo capacity and the capacity within Gaussian communication, showing that Gaussian regime is not sufficient to achieve the Holevo bound particularly in the low-photon regime. Furthermore the Gaussian benchmark established here can be used to critically assess the performance of non-Gaussian protocols for optical communication. We move on to identify non-Gaussian schemes to beat the Gaussian capacity and show that a non-Gaussian receiver recently implemented by Becerra et al. [Nat. Photon. 7, 147 (2013)] can achieve this aim with an appropriately chosen encoding strategy.

arXiv comment9 pages, 6 figures, with supplemental material

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• Multi-Phase Hadamard receivers for classical communication on lossy bosonic channels
• Approximate reversal of quantum Gaussian dynamics
• The capacity of coherent-state adaptive decoders with interferometry and single-mode detectors
• On the classical capacity of quantum Gaussian measurement
• Single-shot non-Gaussian Measurements for Optical Phase Estimation
• Proof of the Gaussian maximizers conjecture for the communication capacity of noisy heterodyne measurements
• Optimal continuous-variable teleportation under energy constraint
• Limit of Gaussian operations and measurements for Gaussian state discrimination, and its application to state comparison
• Non-Gaussian entanglement criteria for atomic homodyne detection
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks