[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125552-en":3,"doc-seo-125552-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},125552,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",6,"Technology","Explainable Quantum Machine Learning - Explainable Quantum Machine Learning","Explainable Quantum Machine Learning investigates how to make quantum machine learning (QML) models understandable to humans by quantifying the importance of (groups of) quantum gates for specific tasks. The work adapts Shapley values from explainable AI to the quantum setting, producing gate-level attributions that explain why a parameterized circuit performs well. Classical parameter optimization of quantum-classical hybrid algorithms is assumed, while experimental results on simulators and superconducting quantum hardware validate benefits for classification, generative modeling, transpilation, and optimization.","arXiv :2301 .09138v1 [ quant-ph] 22 Jan 2023  \nExplainable Quantum Machine Learning  \nRaoul Heese1,*, Thore Gerlach2, Sascha M􀁿ucke3, Sabine M􀁿uller1 , Matthias Jakobs3, and Nico Piatkowski2  \n1 Fraunhofer ITWM, 2 Fraunhofer IAIS, 3 TU Dortmund  \n* [raoul.heese@itwm.fraunhofer.de](raoul.heese@itwm.fraunhofer.de)  \nAbstract  \nMethods of arti􀀌cial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives. This leads to explainable AI (XAI) manifesting itself as an important research 􀀌eld that helps humans to better comprehend ML systems. In parallel, quantum machine learning (QML) is emerging with the ongoing improvement of quantum computing hardware combined with its increasing availability via cloud services. QML enables quantum-enhanced ML in which quantum mechanics is exploited to facilitate ML tasks, typically in form of quantum-classical hybrid algorithms that combine quantum and classical resources. Quantum gates constitute the building blocks of gate-based quantum hardware and form circuits that can be used for quantum computations. For QML applications, quantum circuits are typically parameterized and their parameters are optimized classically such that a suitably de􀀌ned objective function is minimized. Inspired by XAI, we raise the question of explainability of such circuits by quantifying the importance of (groups of) gates for speci􀀌c goals. To this end, we transfer and adapt the well-established concept of Shapley values to the quantum realm. The resulting attributions can be interpreted as explanations for why a speci􀀌c circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general. An experimental evaluation on simulators and two superconducting quantum hardware devices demonstrates the bene􀀌ts of the proposed framework for classi􀀌cation, generative modeling, transpilation, and optimization. Furthermore, our results shed some light on the role of speci􀀌c gates in popular QML approaches.  \n1 Introduction  \nMachine learning (ML) has a signi􀀌cant impact on many applications in di􀀋erent domains. With the ongoing improvement of ML models and their growing complexity, another aspect is becoming increasingly important in addition to pure performance: the explainability of ML systems [1] . Explainability refers to methods that make the behavior of ML systems { or, more generally, arti􀀌cial intelligence (AI) systems [2] { comprehensible for humans. Realizing explainable AI (XAI) is a highly non-trivial task with a potentially great impact on many applications and can therefore be considered as an important research 􀀌eld. For example, XAI can be used to analyze the fairness of models to avoid discrimination, or their security against adversarial attacks, to name just two important aspects. Currently, reasoning about decisions is typically only possible to a very limited extent or even intractable for state-of-the-art ML models. A review of XAI exceeds the scope of this paper and we instead refer to, e. g., [1, 3{6] and references therein.  \nApart from explainability, another promising research direction of ML that has recently arisen with the emergence of new technology is quantum machine learning (QML) [7, 8] . This 􀀌eld addresses how quantum computers (or quantum information processing in general [9]) can be used to provide a bene􀀌t for ML. To identify possible advantages, the structure of the data seems to be key [10{ 12] . However, since currently only noisy intermediate-scale quantum (NISQ) devices [13] with limited capabilities are available, a practical application of QML is restricted to toy examples. On the other hand, the identi􀀌cation of a quantum advantage is not necessarily of central importance for current  \nresearch [14] . Instead, the study of fundamental aspects of QML and their prospect","cbCaiuhz3L6ZEh7w","https://ap.wps.com/l/cbCaiuhz3L6ZEh7w","pdf",1628380,1,35,"English","en",105,"# Abstract\n# Introduction\n# Background\n## Shapley values\n## Quantum machine learning\n# Method and prerequisites (QSVs)\n# Applications (classification, generative modeling, transpilation, optimization)\n# Conclusion and outlook","[{\"question\":\"What problem does Explainable Quantum Machine Learning address?\",\"answer\":\"It addresses the lack of interpretability in quantum machine learning by providing explanations for how and why parameterized quantum circuits work for a given task.\"},{\"question\":\"How are Shapley values used in the proposed approach?\",\"answer\":\"The method transfers and adapts the classical concept of Shapley values to the quantum realm, yielding attributions that quantify the importance of gates or gate groups.\"},{\"question\":\"What advantages are shown through experiments?\",\"answer\":\"Experiments on simulators and superconducting quantum hardware demonstrate benefits for classification, generative modeling, transpilation, and optimization, and clarify the roles of specific gates in common QML methods.\"}]","Explainable Quantum Machine Learning - 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