Real-time phase-reference monitoring of a quasi-optimal coherent-state receiver

Abstract

The Kennedy-like receiver is a quasi-optimal receiver employed in binary phase-shift-keyed communication schemes with coherent states. It is based on the interference of the two signals encoding the message with a reference local oscillator and on/off photodetection. We show both theoretically and experimentally that it is possible to extract useful information about the phase reference by Bayesian processing of the very same data sample used to discriminate the signals shot by shot. We demonstrate that the minimum uncertainty in phase estimation, given by the inverse of the Fisher information associated with the statistics of the collected data, can be achieved. We also numerically and experimentally investigate the performances of our phase-estimation method in the presence of phase noise, when either on/off or photon-number resolving detectors are employed.

5 pages, 6 figures

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• Quantum probes for the cutoff frequency of Ohmic environments
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• Robust Measurement for the Discrimination of Binary Coherent States
• Hybrid quantum key distribution using coherent states and photon-number-resolving detectors
• Experimental demonstration of a quantum receiver beating the standard quantum limit at the telecom wavelength
• Quantum Probes for Ohmic Environments at Thermal Equilibrium
• Homodyne-like detection for state-discrimination in the presence of phase noise
• Squeezing-enhanced phase-shift-keyed binary communication in noisy channels
• Homodyne tomography with homodyne-like detection
• Detection of squeezing by on-chip glass-integrated homodyne analyzer
• Phase tracking for sub-shot-noise-limited receivers
• Squeezing phase diffusion
• Phase noise mitigation by a realistic optical parametric oscillator
• A robust hybrid receiver for binary phase-shift keying discrimination in the presence of phase noise
• On the application of a Silicon photomultiplier-based receiver for binary phase-shift-keying protocols
• Channel-noise tracking for sub-shot-noise-limited receivers with neural networks