Leonhard Kunczik (35 results)

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Paperback. Condition: new. Paperback. This book constitutes the refereed proceedings of the 17th International Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 1416, 2022.The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. 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. This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condition: Brand New. 289 pages. 9.25x6.10x0.61 inches. In Stock.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 17thInternational Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 14-16, 2022.The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services.

Condition: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Paperback. Condition: new. Paperback. This book constitutes the refereed proceedings of the 17th International Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 1416, 2022.The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. Critical Information Infrastructures Security | 17th International Conference, CRITIS 2022, Munich, Germany, September 14-16, 2022, Revised Selected Papers | Bernhard Hämmerli (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xiii | Englisch | 2023 | Springer | EAN 9783031351891 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Condition: Hervorragend. Zustand: Hervorragend | Seiten: 292 | Sprache: Englisch | Produktart: Bücher | This book constitutes the refereed proceedings of the 17th International Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 14ż16, 2022.The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services.

Taschenbuch. Condition: Neu. Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context | Leonhard Kunczik | Taschenbuch | xviii | Englisch | 2022 | Springer VS | EAN 9783658376154 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Paperback. Condition: new. Paperback. 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. This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Softcover. Condition: gut. 2023. Critical Information Infrastructures Security In deutscher Sprache. pages.

Paperback. Condition: Brand New. 289 pages. 9.25x6.10x0.61 inches. In Stock. This item is printed on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the 17thInternational Conference on Critical Information Infrastructures Security, CRITIS 2022, which took place in Munich, Germany, during September 14-16, 2022.The 16 full papers and 4 short papers included in this volume were carefully reviewed and selected from 26 submissions. They are organized in topical sections as follows: protection of cyber-physical systems and industrial control systems (ICS); C(I)IP organization, (strategic) management and legal aspects; human factor, security awareness and crisis management for C(I)IP and critical services; and future, TechWatch and forecast for C(I)IP and critical services. 292 pp. Englisch.

Condition: New. Print on Demand pp. 292.

Condition: New. PRINT ON DEMAND pp. 292.

Paperback. Condition: Brand New. 152 pages. 8.27x5.83x0.36 inches. In Stock. This item is printed on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 152 pp. Englisch.