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دانلود کتاب Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
دانلود کتاب یادگیری تقویتی با تقریب کوانتومی ترکیبی در زمینه NISQ
مشخصات کتاب
Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
ویرایش:
نویسندگان: Leonhard Kunczik
سری:
ISBN (شابک) : 9783658376154, 3658376155
ناشر: Springer Vieweg
سال نشر: 2022
تعداد صفحات: 152
[145]
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 3 Mb
قیمت کتاب (تومان) : 60,000
در صورت تبدیل فایل کتاب Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب یادگیری تقویتی با تقریب کوانتومی ترکیبی در زمینه NISQ نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب یادگیری تقویتی با تقریب کوانتومی ترکیبی در زمینه NISQ
توضیحاتی درمورد کتاب به خارجی
This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today\'s NISQ hardware, the algorithm is evaluated on IBM\'s quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning.
فهرست مطالب
Abstract Acknowledgements Contents List of Figures List of Tables 1 Motivation: Complex Attacker-Defender Scenarios—The Eternal Conflict 1.1 Reinforcement Learning and Attacker-Defender Scenarios 1.1.1 Today's Challenges in Reinforcement Learning and More Complex Attacker-Defender Scenarios 1.2 Quantum Computing—An Opportunity for Reinforcement Learning in Complex Attacker-Defender Scenarios 1.3 Specifying the Research Questions 2 The Information Game—A special Attacker-Defender Scenario 2.1 The 2D Game 2.2 High Complexity 3D Game 3 Reinforcement Learning and Bellman's Principle of Optimality 3.1 A Short Mathematical Introduction to Reinforcement Learning 3.1.1 Value Functions and Bellman's Principle of Optimality 3.2 From Bellman to Q-learning—The Tabular Approach 3.3 Approximation Techniques in Reinforcement Learning 3.3.1 DQN—Advanced Value Approximation 3.3.2 Policy Gradient—Policy Approximation 3.4 Policy vs. Value-based Methods in Attacker-Defender Scenarios 4 Quantum Reinforcement Learning—Connecting Reinforcement Learning and Quantum Computing 4.1 Quantum Reinforcement Learning Methods 4.2 Projective Simulation Methods 4.3 Quantum Hybrid Approximation Methods 4.4 Defining the Research Questions 5 Approximation in Quantum Computing 5.1 Quantum Variational Circuits—A Quantum Approximator 6 Advanced Quantum Policy Approximation in Policy Gradient Reinforcement Learning 6.1 Central idea: Quantum Variational Circuits' Components 6.1.1 Parameter Encoding 6.1.2 Variational Form 6.1.3 Post Processing 6.2 Experimental Framework & Hyper-Parameter Optimization 7 Applying Quantum REINFORCE to the Information Game 7.1 Experimental Set-Up: Optimal Parameter Configuration 7.2 Results 7.2.1 The Simple Problem—A First Approach 7.2.2 Enlarging the State Space—The 2D8 Game 7.2.3 High Complexity with the 3D Game 7.3 Discussion 8 Evaluating Quantum REINFORCE on IBM's Quantum Hardware 8.1 Evaluating the Trained Algorithm 8.2 The Full Training Loop on the Quantum Hardware 8.3 Increasing the Level of Detail 8.4 Summary and Answering the Research Question 9 Future Steps in Quantum Reinforcement Learning for Complex Scenarios 9.1 Characteristics of NISQ devices 9.2 Improved Data Encoding 9.3 Analysis of Quantum Variational Circuits in Quantum Policy Gradient methods 10 Conclusion A Bibliography
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