[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117093-en":3,"doc-seo-117093-105":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},117093,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Exploratory Quantum Machine Learning Techniques For Hybrid Systems","Dominic Pasquali presents exploratory quantum machine learning methods tailored to hybrid systems, combining quantum computation, classical neural-network training concepts, and spiking neural network dynamics. The dissertation surveys foundations in quantum computing and machine learning, then develops new approaches including classical and quantum self-attention mechanisms and a learning framework for unimodular matrices used in quantum circuits. It also proposes integrated spiking and quantum neural network architectures, reporting experiments and results to evaluate performance and provide discussion.","UC Santa Cruz  \nUC Santa Cruz Electronic Theses and Dissertations  \nTitle  \nExploratory Quantum Machine Learning Techniques For Hybrid Systems  \nPermalink  \n[https://escholarship.org/uc/item/7dt5k8v6](https://escholarship.org/uc/item/7dt5k8v6)  \nAuthor  \nPasquali, Dominic  \nPublication Date  \n2023  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution License, available at [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \n[Peer reviewed|Thesis/dissertation](Peer reviewed|Thesis/dissertation)  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nSANTA CRUZ  \nEXPLORATORY QUANTUM MACHINE LEARNING TECHNIQUES FOR HYBRID SYSTEMS  \nA dissertation submitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nPHYSICS  \nby  \nDominic Pasquali  \nDecember 2023  \nThe Dissertation of Dominic Pasqualiis approved:  \nDr. Joshua Deutsch, Chair  \nDr. Wolfgang Altmannshofer  \nDr. Andrew Coats  \nPeter Biehl  \nVice Provost and Dean of Graduate Studies  \nCopyright © by Dominic Pasquali 2023  \nTable of Contents  \nList of Figures vi  \nList of Tables xi  \nAbstract xii  \nDedication xiii  \nAcknowledgments xiv  \nI Introduction 1  \n1 Introduction 2  \nII Foundations 6  \n2 Quantum Computing 7  \n2.1 Quantum Motivation From Classical Computing ............. 7  \n2.2 A Reminder Of Classical Computing .................... 8  \n2.3 Select Elements Of Quantum Computing ................. 8  \n2.4 Motivation For Exploring Quantum Computing .............. 16  \n3 Machine Learning Overview: Classical, Quantum, and Spiking Neurons 19  \n3.1 Classical Supervised Machine Learning ................... 19  \n3.1.1 A Brief Reminder Of Supervised Machine Learning Components 19  \n3.1.2 Linear Relationships ......................... 23  \n3.1.3 Introducing Non-Linear Functions ................. 25  \n3.1.4 The Artificial Neuron ........................ 26  \n3.1.5 Artificial Neural Networks ...................... 27  \n3.1.6 Understanding Loss ......................... 28  \niii  \n3.1.7 Backpropagation By Example .................... 29  \n3.1.8 Updating Parameters ........................ 31  \n3.2 Quantum Machine Learning ......................... 33  \n3.2.1 Parameters To Optimize ....................... 33  \n3.2.2 Gradients In Quantum Systems ................... 34  \n3.3 Spiking Neural Networks ........................... 35  \n3.3.1 A New Generation: Spiking Neural Networks ........... 35  \n3.3.2 A Review Of Select Neuron Anatomy And Function ....... 37  \n3.3.3 Introduction To The Leaky Integrate And Fire Neuron Model .. 40  \n3.3.4 Discretely Solving The ODE .................... 43  \n3.3.5 An Analytical β ........................... 46  \n3.3.6 Elements Of Spiking Neural Networks ............... 49  \n3.3.7 Surrogate Gradients ......................... 52  \n3.3.8 Motivations For Spiking Neural Networks ............. 54  \nIII New Ideas And Applications 58  \n4 Classical And Quantum Self-Attention 59  \n4.1 The Rise Of Self-Attention Architectures .................. 59  \n4.2 Previous Quantum Self-Attention Architectures .............. 62  \n4.3 Methodology ................................. 64  \n4.3.1 Quantum Self-Attention ....................... 65  \n4.3.2 Creating The Attention Mask .................... 65  \n4.3.3 Generating The Output ....................... 66  \n4.4 Software, Data, Models & Experiment ................... 67  \n4.4.1 Software and Data .......................... 67  \n4.4.2 Models ................................ 69  \n4.5 Results And Discussion ........................... 71  \n4.6 Remarks .................................... 75  \n5 Learning Unimodular Matrices For Quantum Circuits 76  \n5.1 Introduction .................................. 76  \n5.2 Previous Work ................................ 77  \n5.3 Ansatz & Methodologies ........................... 77  \n5.3.1 Murn","cbCaidCcWUIq2pop","https://ap.wps.com/l/cbCaidCcWUIq2pop","pdf",15691363,1,148,"English","en",105,"# I Introduction\n## 1 Introduction\n# II Foundations\n## 2 Quantum Computing\n## 3 Machine Learning Overview: Classical, Quantum, and Spiking Neurons\n# III New Ideas And Applications\n## 4 Classical And Quantum Self-Attention\n## 5 Learning Unimodular Matrices For Quantum Circuits\n## 6 Integrated Spiking And Quantum Neural Networks\n# IV Conclusion\n## 7 Conclusion","[{\"question\":\"这篇论文研究的核心主题是什么？\",\"answer\":\"论文围绕面向混合系统的探索性量子机器学习技术展开，结合量子计算、经典机器学习训练思路与脉冲神经网络建模。\"},{\"question\":\"论文在基础部分如何衔接量子计算与机器学习？\",\"answer\":\"论文先回顾量子计算的相关概念与动机，再梳理经典监督学习、量子机器学习中的可优化参数与梯度，以及脉冲神经网络（含LIF模型与替代梯度）。\"},{\"question\":\"论文提出了哪些新的方法与应用章节？\",\"answer\":\"新想法主要包括经典与量子自注意力、用于量子电路的保模（unimodular）矩阵学习，以及融合脉冲与量子神经网络的集成架构，并给出实验与讨论结果。\"}]","Exploratory Quantum Machine Learning Techniques For Hybrid Systems | 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