Optical decoder learning for fiber communication at the quantum limit
Matteo Rosati, Albert Solana

TL;DR
This paper introduces a supervised-learning framework to design optical joint-detection receivers for quantum-enhanced fiber communication, achieving significant improvements in decoding rates close to theoretical limits.
Contribution
The authors develop a systematic, machine-learning-based method to discover new optical joint-detection receiver designs for quantum communication, surpassing previous designs in performance.
Findings
Up to 3-fold increase in bit decoding rate over single-symbol receivers.
Achieved performance within 7% of the theoretical optimal decoder.
Discovered optical circuit setups for maximum-size codes and small message-lengths.
Abstract
Quantum information theory predicts that communication technology can be enhanced by using quantum signals to transfer classical bits. In order to fulfill this promise, the message-carrying signals must interact coherently at the decoding stage via a joint-detection receiver (JDR), whose realization with optical technologies remains an outstanding open problem to date. We introduce a supervised-learning framework for the systematic discovery of new JDR designs based on parametrized photonic integrated circuits. Our framework relies on the synthesis of a training set comprising quantum codewords and the corresponding classical message label; the codewords are processed by the JDR circuit and, after photo-detection, produce a guess for the label. The circuit parameters are then updated by minimizing a suitable loss function, reaching an optimal JDR design for that specific architecture.…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Optical Network Technologies
