[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117918-en":3,"doc-seo-117918-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117918,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Review of the Applications of Quantum Machine Learning in Optical Communication Systems","Optical signal processing can leverage quantum and quantum-inspired machine learning to address challenges in decoding and error correction. Non-linear or unknown noise may bypass conventional linear error-correction strategies used in optical receivers, so learning-based estimation can reconstruct the transmitted signal from received observations. The review surveys proposed quantum and quantum-inspired machine learning algorithms and evaluates their suitability for current optical signal-processing technologies. The discussion also contextualizes where potential quantum advantage arises over classical methods, including complexity and accuracy considerations.","arXiv :2309 .05205v2 [ quant-ph] 25 Sep 2023  \nA Review of the Applications of Quantum Machine Learning in Optical Communication Systems  \nArk Modi§􀀃 (A.M.), Alonso Viladomat Jasso§†(A.V.J.), Roberto Ferrara􀀃 (R.F.), Christian Deppe􀀃 (C.D.), Janis Nötzel†(J.N), Fred Fung‡(F.F.), Maximilian Schädler‡(M.S.)  \n􀀃 Institute for Communications Engineering (LNT),† Emmy Noether Group for Theoretical Quantum Systems Design  \nTechnical University of Munich, D-80333 Munich, Germany  \n‡ Optical and Quantum Laboratory, Munich Research Center  \nHuawei Technologies Düsseldorf GmbH, Riesstr. 25-C3,80992 Munich, Germany  \n§ These authors contributed equally to the work  \nEmail: {ark.modi, viladomat.jasso, roberto.ferrara, christian.deppe, [janis.noetzel}@tum.de](janis.noetzel}@tum.de)[ ](janis.noetzel}@tum.de){fred.fung, [maximilian.schaedler}@huawei.com](maximilian.schaedler}@huawei.com)  \nAbstract—In the context of optical signal processing, quantum and quantum-inspired machine learning algorithms have massive potential for deployment. One of the applications is in error correction protocols for the received noisy signals. In some scenarios, non-linear and unknown errors can lead to noise that bypasses linear error correction protocols that optical receivers generally implement. In those cases, machine learning techniques are used to recover the transmitted signal from the received signal through various estimation procedures. Since quantum machine learning algorithms promise advantage over classical algorithms, we expect that optical signal processing can bene􀀂t from these advantages. In this review, we survey several proposed quantum and quantum-inspired machine learning algorithms and their applicability with current technology to optical signal processing.  \nIndex Terms—Quantum Machine Learning, Quantum Algorithms, Quantum Computing, 6G Communication, QuantumClassical Hybrid Algorithms, Optical Communication  \nI. SUMMARY  \nArti􀀂cial intelligence has made signi􀀂cant progress thanks to modern large-scale machine learning (henceforth referred to as ML), leading to the deployment of weakly intelligent cognitive systems in various aspects of daily and professional life. ML involves adjusting software agent parameters through training processes, allowing them to develop problem-solving skills. This progress relies on analyzing large amounts of task-speci􀀂c training data to learn desired input-output behaviours. The success of modern ML is largely attributed to working with domain-agnostic models and training algorithms, with deep learning being especially successful. Deep learning utilizes arti􀀂cial neural networks with billions of adjustable parameters, making them 􀀃exible and effective in various computational intelligence tasks. However, training deep neural networks requires vast amounts of representative data and considerable computational resources. To train stateof-the-art systems effectively, like OpenAI’s GPT-3, dedicated compute clusters and high-performance computing hardware are necessary due to the immense computational demands. Asa result, the practical feasibility and success of current ML  \napplications are highly dependent on access to such advanced computing resources.  \nResearchers are increasingly exploring quantum computing as a potential solution to the computational demands of modern ML systems. A “quantum advantage\" can manifest in various ways, primarily affecting time complexity or execution time, and accuracy. Quantum computing has made signi􀀂cant strides and promises faster computations in scienti􀀂c and industrial applications. A number of works such as [1]–[7] claim to achieve a time advantage while works such as [8]–[12] show accuracy and convergence gains. Quantum computers operate on qubits, which exist in superposition and can carry more information than classical bits. Computation with qubits is probabilistic, and measurements cause decoherence, collapsing the qubit to a speci􀀂c state. Quantum bits can beentan","cbCainDS848zms8v","https://ap.wps.com/l/cbCainDS848zms8v","pdf",140553,1,6,"English","en",105,"# Summary\n## Machine learning progress and practical constraints\n## Quantum computing and sources of quantum advantage\n## QML in the QML/NISQ/QRAM context\n## Entanglement and sampling advantages\n## Adiabatic quantum computing for optimization","[{\"question\":\"Why can quantum and quantum-inspired machine learning help optical signal processing?\",\"answer\":\"They offer learning and estimation approaches that can recover transmitted signals when optical channels introduce non-linear or unknown errors that evade linear error-correction protocols.\"},{\"question\":\"How does the review frame “quantum advantage” over classical algorithms?\",\"answer\":\"It explains advantage in terms of execution time (time complexity) and accuracy, highlighting different sources such as entanglement and sampling properties of quantum systems.\"},{\"question\":\"What roles do entanglement and sampling play in quantum machine learning performance?\",\"answer\":\"Sampling advantage is noted for certain linear-algebraic QML procedures, while entanglement supports complex correlations and quantum parallelism, enabling more effective representation and faster processing of some computations.\"}]","A Review of the Applications of Quantum Machine Learning in Optical Communication Systems | 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can quantum and quantum-inspired machine learning help optical signal processing?","Question",{"text":76,"@type":77},"They offer learning and estimation approaches that can recover transmitted signals when optical channels introduce non-linear or unknown errors that evade linear error-correction protocols.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the review frame “quantum advantage” over classical algorithms?",{"text":81,"@type":77},"It explains advantage in terms of execution time (time complexity) and accuracy, highlighting different sources such as entanglement and sampling properties of quantum systems.",{"name":83,"@type":74,"acceptedAnswer":84},"What roles do entanglement and sampling play in quantum machine learning performance?",{"text":85,"@type":77},"Sampling advantage is noted for certain linear-algebraic QML procedures, while entanglement supports complex correlations and quantum parallelism, enabling more effective representation and faster processing 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