Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Optical Fiber Communication Conference

Advancing classical and quantum communication systems with machine learning

Darko Zibar, Uiara Celine de Moura, Hou-Man Chin, A. M. Rosa Brusin, Nitin Jain, Francesco Da Ros, Sebastian Kleis, C. Schaeffer, Tobias Gehring, Ulrik L. Andersen, Andrea Carena

Published Jan 1, 2020• 1 citation
Publisher

Abstract

A perspective on how machine learning can aid the next–generation of classical and quantum optical communication systems is given. We focus on the design of Raman amplifiers and phase tracking at the quantum limit.
DOI
10.1364/ofc.2020.w1k.1

Related papers

Artificial intelligence and machine learning for quantum technologies

Mario Krenn, Jonas Landgraf, Thomas Fösel +1 more

In this Perspective, the authors review how machine learning, and more broadly methods of artificial intelligence, are utilized in advancing quantum technologies, specifically the design, control, calibration and optimization of quantum devices. They also discuss open challenges in the field and potential future directions within the next decade.

arXiv:2208.03836•Physical Review A•Jan 3, 2023•163 citations

Empowering Quantum 2.0 Devices and Approaches with Machine Learning

Blake Wilson, Yuheng Chen, Sabre Kais +3 more

We present recent advances and future perspectives in using machine learning for characterization, fabrication, and inverse design for device applications, such as hybrid quantum-classical optimization of nanostructures, hypothesis learning for automated discovery, and pre-characterization binning.

Quantum 2.0 Conference and Exhibition•Jan 1, 2022•2 citations

Coherent optical communications enhanced by machine intelligence

Sanjaya Lohani, Ryan T Glasser

Abstract Accuracy in discriminating between different received coherent signals is integral to the operation of many free-space communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communication scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we design the neural network system such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is distributed to the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.

arXiv:1909.02525•Machine Learning Science and Technology•Jun 12, 2020•10 citations

Toward Autonomous Quantum Communication

Marric Stephens

A machine-learning algorithm previously used to solve navigation problems can devise efficient ways to transmit quantum information.

Physics•Sep 3, 2020•0 citations

Friendly Introduction to Quantum and Quantum-Inspired Machine Learning

Ema Puljak

Dr. Ema Puljak Bio: Ema Puljak is a Quantum Algorithms Expert at PlanQC, working on quantum and quantum-inspired machine learning for real-world applications. She completed a collaborative PhD between CERN, the Barcelona Supercomputing Center, and Universitat Autònoma de Barcelona, focusing on improving anomaly detection methods using quantum and tensor-network techniques for medical imaging and high-energy physics. Passionate about science communication, she regularly delivers accessible talks and tutorials on quantum computing, tensor networks, and quantum machine learning. TITLE: Friendly Introduction to Quantum and Quantum-Inspired Machine Learning Abstract: This talk introduces quantum and quantum-inspired machine learning from a practical perspective. We start with a brief overview of classical ML, then move to quantum-inspired approaches based on tensor networks, which already enable efficient representations and compression on classical hardware. Next, we cover quantum machine learning, including variational circuits, quantum kernels, and hybrid quantum–classical workflows, highlighting where quantum models may assist (rather than replace) classical ML. We close with real-world application outlooks for both paradigms and open questions around scalability, and future perspectives, including whether demonstrating quantum advantage should be the only goal of quantum machine learning.

•Mar 7, 2026•0 citations

Journal of Selected Topics in Quantum Electronics on Machine Learning in Photonic Communication and Measurement Systems

IEEE Journal of Selected Topics in Quantum Electronics•Sep 1, 2020•0 citations
Abstract Accuracy in discriminating between different received coherent signals is integral to the operation of many free-space communications protocols, and is often difficult when the receiver measures a weak signal. Here we design an optical communication scheme that uses balanced homodyne detection in combination with an unsupervised generative machine learning and convolutional neural network (CNN) system, and demonstrate its efficacy in a realistic simulated coherent quadrature phase shift keyed (QPSK) communications system. Additionally, we design the neural network system such that it autonomously learns to correct for the noise associated with a weak QPSK signal, which is distributed to the receiver prior to the implementation of the communications. We find that the scheme significantly reduces the overall error probability of the communications system, achieving the classical optimal limit. We anticipate that these results will allow for a significant enhancement of current classical and quantum coherent optical communications technologies.
Dr. Ema Puljak Bio: Ema Puljak is a Quantum Algorithms Expert at PlanQC, working on quantum and quantum-inspired machine learning for real-world applications. She completed a collaborative PhD between CERN, the Barcelona Supercomputing Center, and Universitat Autònoma de Barcelona, focusing on improving anomaly detection methods using quantum and tensor-network techniques for medical imaging and high-energy physics. Passionate about science communication, she regularly delivers accessible talks and tutorials on quantum computing, tensor networks, and quantum machine learning. TITLE: Friendly Introduction to Quantum and Quantum-Inspired Machine Learning Abstract: This talk introduces quantum and quantum-inspired machine learning from a practical perspective. We start with a brief overview of classical ML, then move to quantum-inspired approaches based on tensor networks, which already enable efficient representations and compression on classical hardware. Next, we cover quantum machine learning, including variational circuits, quantum kernels, and hybrid quantum–classical workflows, highlighting where quantum models may assist (rather than replace) classical ML. We close with real-world application outlooks for both paradigms and open questions around scalability, and future perspectives, including whether demonstrating quantum advantage should be the only goal of quantum machine learning.