Reinforcement learning with hybrid quantum approximation in the NISQ context:
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| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Wiesbaden, Germany
Springer Vieweg
[2022]
|
| Series: | Research
Moremedia |
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Item Description: | Literaturverzeichnis Seite 127-134 |
| Physical Description: | xviii, 134 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 211 g |
| ISBN: | 9783658376154 3658376155 |
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| 245 | 1 | 0 | |a Reinforcement learning with hybrid quantum approximation in the NISQ context |c Leonhard Kunczik |
| 264 | 1 | |a Wiesbaden, Germany |b Springer Vieweg |c [2022] | |
| 264 | 4 | |c © 2022 | |
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Record in the Search Index
| _version_ | 1804184171434737664 |
|---|---|
| adam_text | CONTENTS
1
MOTIVATION:
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
-
THE
ETERNAL
CONFLICT
...............................................................................
1
1.1
REINFORCEMENT
LEARNING
AND
ATTACKER-DEFENDER
SCENARIOS
....
4
1.1.1
TODAY
S
CHALLENGES
IN
REINFORCEMENT
LEARNING
AND
MORE
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
..........
6
1.2
QUANTUM
COMPUTING
-
AN
OPPORTUNITY
FOR
REINFORCEMENT
LEARNING
IN
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
..........
9
1.3
SPECIFYING
THE
RESEARCH
QUESTIONS
..............................................
11
2
THE
INFORMATION
GAME
-
A
SPECIAL
ATTACKER-DEFENDER
SCENARIO
............................................................................................
15
2.1
THE
2D
GAME
.................................................................................
17
2.2
HIGH
COMPLEXITY
3D
GAME
..........................................................
20
3
REINFORCEMENT
LEARNING
AND
BELLMAN S
PRINCIPLE
OF
OPTIMALITY
...................................................................................
23
3.1
A
SHORT
MATHEMATICAL
INTRODUCTION
TO
REINFORCEMENT
LEARNING
..................................................................................
24
3.1.1
VALUE
FUNCTIONS
AND
BELLMAN
S
PRINCIPLE
OF
OPTIMALITY
......................................................................
27
3.2
FROM
BELLMAN
TO
Q-LEARNING
THE
TABULAR
APPROACH
.............
30
3.3
APPROXIMATION
TECHNIQUES
IN
REINFORCEMENT
LEARNING
...........
32
3.3.1
DQN
-
ADVANCED
VALUE
APPROXIMATION
.........................
33
3.3.2
POLICY
GRADIENT
-
POLICY
APPROXIMATION
.........................
35
3.4
POLICY
VS.
VALUE-BASED
METHODS
IN
ATTACKER-DEFENDER
SCENARIOS
................................................................................
38
IX
X
CONTENTS
4
QUANTUM
REINFORCEMENT
LEARNING
-
CONNECTING
REINFORCEMENT
LEARNING
AND
QUANTUM
COMPUTING
.......................
41
4.1
QUANTUM
REINFORCEMENT
LEARNING
METHODS
...............................
42
4.2
PROJECTIVE
SIMULATION
METHODS
.....................................................
43
4.3
QUANTUM
HYBRID
APPROXIMATION
METHODS
.................................
45
4.4
DEFINING
THE
RESEARCH
QUESTIONS
...................................................
46
5
APPROXIMATION
IN
QUANTUM
COMPUTING
.............................................
49
5.1
QUANTUM
VARIATIONAL
CIRCUITS
-
A
QUANTUM
APPROXIMATOR
....
50
6
ADVANCED
QUANTUM
POLICY
APPROXIMATION
IN
POLICY
GRADIENT
REINFORCEMENT
LEARNING
........................................................................
55
6.1
CENTRAL
IDEA:
QUANTUM
VARIATIONAL
CIRCUITS
COMPONENTS
........
58
6.1.1
PARAMETER
ENCODING
..........................................................
59
6.1.2
VARIATIONAL
FORM
................................................................
60
6.1.3
POST
PROCESSING
..................................................................
61
6.2
EXPERIMENTAL
FRAMEWORK
&
HYPER-PARAMETER
OPTIMIZATION
......................................................................................
63
7
APPLYING
QUANTUM
REINFORCE
TO
THE
INFORMATION
GAME
..........
65
7.1
EXPERIMENTAL
SET-UP:
OPTIMAL
PARAMETER
CONFIGURATION
...........
65
7.2
RESULTS
.............................................................................................
68
7.2.1
THE
SIMPLE
PROBLEM
-
A
FIRST
APPROACH
........................
69
7.2.2
ENLARGING
THE
STATE
SPACE
-
THE
2D8
GAME
..................
75
7.2.3
HIGH
COMPLEXITY
WITH
THE
3D
GAME
...............................
80
7.3
DISCUSSION
.......................................................................................
85
8
EVALUATING
QUANTUM
REINFORCE
ON
IBM
S
QUANTUM
HARDWARE
..................................................................................................
91
8.1
EVALUATING
THE
TRAINED
ALGORITHM
...................................................
91
8.2
THE
FULL
TRAINING
LOOP
ON
THE
QUANTUM
HARDWARE
..................
95
8.3
INCREASING
THE
LEVEL
OF
DETAIL
........................................................
98
8.4
SUMMARY
AND
ANSWERING
THE
RESEARCH
QUESTION
........................
112
9
FUTURE
STEPS
IN
QUANTUM
REINFORCEMENT
LEARNING
FOR
COMPLEX
SCENARIOS
....................................................................
115
9.1
CHARACTERISTICS
OF
NISQ
DEVICES
...................................................
116
9.2
IMPROVED
DATA
ENCODING
..............................................................
118
CONTENTS
XI
9.3
ANALYSIS
OF
QUANTUM
VARIATIONAL
CIRCUITS
IN
QUANTUM
POLICY
GRADIENT
METHODS
......................................................
120
10
CONCLUSION
...............................................................................................
121
BIBLIOGRAPHY
....................................................................................................
127
|
| adam_txt |
CONTENTS
1
MOTIVATION:
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
-
THE
ETERNAL
CONFLICT
.
1
1.1
REINFORCEMENT
LEARNING
AND
ATTACKER-DEFENDER
SCENARIOS
.
4
1.1.1
TODAY
'
S
CHALLENGES
IN
REINFORCEMENT
LEARNING
AND
MORE
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
.
6
1.2
QUANTUM
COMPUTING
-
AN
OPPORTUNITY
FOR
REINFORCEMENT
LEARNING
IN
COMPLEX
ATTACKER-DEFENDER
SCENARIOS
.
9
1.3
SPECIFYING
THE
RESEARCH
QUESTIONS
.
11
2
THE
INFORMATION
GAME
-
A
SPECIAL
ATTACKER-DEFENDER
SCENARIO
.
15
2.1
THE
2D
GAME
.
17
2.2
HIGH
COMPLEXITY
3D
GAME
.
20
3
REINFORCEMENT
LEARNING
AND
BELLMAN'S
PRINCIPLE
OF
OPTIMALITY
.
23
3.1
A
SHORT
MATHEMATICAL
INTRODUCTION
TO
REINFORCEMENT
LEARNING
.
24
3.1.1
VALUE
FUNCTIONS
AND
BELLMAN
'
S
PRINCIPLE
OF
OPTIMALITY
.
27
3.2
FROM
BELLMAN
TO
Q-LEARNING
THE
TABULAR
APPROACH
.
30
3.3
APPROXIMATION
TECHNIQUES
IN
REINFORCEMENT
LEARNING
.
32
3.3.1
DQN
-
ADVANCED
VALUE
APPROXIMATION
.
33
3.3.2
POLICY
GRADIENT
-
POLICY
APPROXIMATION
.
35
3.4
POLICY
VS.
VALUE-BASED
METHODS
IN
ATTACKER-DEFENDER
SCENARIOS
.
38
IX
X
CONTENTS
4
QUANTUM
REINFORCEMENT
LEARNING
-
CONNECTING
REINFORCEMENT
LEARNING
AND
QUANTUM
COMPUTING
.
41
4.1
QUANTUM
REINFORCEMENT
LEARNING
METHODS
.
42
4.2
PROJECTIVE
SIMULATION
METHODS
.
43
4.3
QUANTUM
HYBRID
APPROXIMATION
METHODS
.
45
4.4
DEFINING
THE
RESEARCH
QUESTIONS
.
46
5
APPROXIMATION
IN
QUANTUM
COMPUTING
.
49
5.1
QUANTUM
VARIATIONAL
CIRCUITS
-
A
QUANTUM
APPROXIMATOR
.
50
6
ADVANCED
QUANTUM
POLICY
APPROXIMATION
IN
POLICY
GRADIENT
REINFORCEMENT
LEARNING
.
55
6.1
CENTRAL
IDEA:
QUANTUM
VARIATIONAL
CIRCUITS
'
COMPONENTS
.
58
6.1.1
PARAMETER
ENCODING
.
59
6.1.2
VARIATIONAL
FORM
.
60
6.1.3
POST
PROCESSING
.
61
6.2
EXPERIMENTAL
FRAMEWORK
&
HYPER-PARAMETER
OPTIMIZATION
.
63
7
APPLYING
QUANTUM
REINFORCE
TO
THE
INFORMATION
GAME
.
65
7.1
EXPERIMENTAL
SET-UP:
OPTIMAL
PARAMETER
CONFIGURATION
.
65
7.2
RESULTS
.
68
7.2.1
THE
SIMPLE
PROBLEM
-
A
FIRST
APPROACH
.
69
7.2.2
ENLARGING
THE
STATE
SPACE
-
THE
2D8
GAME
.
75
7.2.3
HIGH
COMPLEXITY
WITH
THE
3D
GAME
.
80
7.3
DISCUSSION
.
85
8
EVALUATING
QUANTUM
REINFORCE
ON
IBM
'
S
QUANTUM
HARDWARE
.
91
8.1
EVALUATING
THE
TRAINED
ALGORITHM
.
91
8.2
THE
FULL
TRAINING
LOOP
ON
THE
QUANTUM
HARDWARE
.
95
8.3
INCREASING
THE
LEVEL
OF
DETAIL
.
98
8.4
SUMMARY
AND
ANSWERING
THE
RESEARCH
QUESTION
.
112
9
FUTURE
STEPS
IN
QUANTUM
REINFORCEMENT
LEARNING
FOR
COMPLEX
SCENARIOS
.
115
9.1
CHARACTERISTICS
OF
NISQ
DEVICES
.
116
9.2
IMPROVED
DATA
ENCODING
.
118
CONTENTS
XI
9.3
ANALYSIS
OF
QUANTUM
VARIATIONAL
CIRCUITS
IN
QUANTUM
POLICY
GRADIENT
METHODS
.
120
10
CONCLUSION
.
121
BIBLIOGRAPHY
.
127 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kunczik, Leonhard |
| author_GND | (DE-588)1261934989 |
| author_facet | Kunczik, Leonhard |
| author_role | aut |
| author_sort | Kunczik, Leonhard |
| author_variant | l k lk |
| building | Verbundindex |
| bvnumber | BV048316757 |
| classification_rvk | ST 300 ST 152 |
| ctrlnum | (OCoLC)1335403405 (DE-599)DNB1254160140 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV048316757 |
| illustrated | Illustrated |
| index_date | 2024-07-03T20:10:56Z |
| indexdate | 2024-07-10T09:35:06Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658376154 3658376155 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033696216 |
| oclc_num | 1335403405 |
| open_access_boolean | |
| owner | DE-706 DE-83 |
| owner_facet | DE-706 DE-83 |
| physical | xviii, 134 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 211 g |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer Vieweg |
| record_format | marc |
| series2 | Research Moremedia |
| spelling | Kunczik, Leonhard Verfasser (DE-588)1261934989 aut Reinforcement learning with hybrid quantum approximation in the NISQ context Leonhard Kunczik Wiesbaden, Germany Springer Vieweg [2022] © 2022 xviii, 134 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 211 g txt rdacontent n rdamedia nc rdacarrier Research Moremedia Literaturverzeichnis Seite 127-134 Dissertation Universität der Bundeswehr München, Neubiberg 2021 Computersicherheit (DE-588)4274324-2 gnd rswk-swf Quantencomputer (DE-588)4533372-5 gnd rswk-swf Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd rswk-swf Quantum Machine Learning Quantum Reinforcement Learning Quanten Computing Reinforcement Learning Attacker-Defender Scenarios (DE-588)4113937-9 Hochschulschrift gnd-content Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 s Quantencomputer (DE-588)4533372-5 s Computersicherheit (DE-588)4274324-2 s DE-604 Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 978-3-658-37616-1 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=9aa5f6f2f7a44362a77ddcce355720f0&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033696216&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20220327 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Kunczik, Leonhard Reinforcement learning with hybrid quantum approximation in the NISQ context Computersicherheit (DE-588)4274324-2 gnd Quantencomputer (DE-588)4533372-5 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| subject_GND | (DE-588)4274324-2 (DE-588)4533372-5 (DE-588)4825546-4 (DE-588)4113937-9 |
| title | Reinforcement learning with hybrid quantum approximation in the NISQ context |
| title_auth | Reinforcement learning with hybrid quantum approximation in the NISQ context |
| title_exact_search | Reinforcement learning with hybrid quantum approximation in the NISQ context |
| title_exact_search_txtP | Reinforcement learning with hybrid quantum approximation in the NISQ context |
| title_full | Reinforcement learning with hybrid quantum approximation in the NISQ context Leonhard Kunczik |
| title_fullStr | Reinforcement learning with hybrid quantum approximation in the NISQ context Leonhard Kunczik |
| title_full_unstemmed | Reinforcement learning with hybrid quantum approximation in the NISQ context Leonhard Kunczik |
| title_short | Reinforcement learning with hybrid quantum approximation in the NISQ context |
| title_sort | reinforcement learning with hybrid quantum approximation in the nisq context |
| topic | Computersicherheit (DE-588)4274324-2 gnd Quantencomputer (DE-588)4533372-5 gnd Bestärkendes Lernen Künstliche Intelligenz (DE-588)4825546-4 gnd |
| topic_facet | Computersicherheit Quantencomputer Bestärkendes Lernen Künstliche Intelligenz Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=9aa5f6f2f7a44362a77ddcce355720f0&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033696216&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kunczikleonhard reinforcementlearningwithhybridquantumapproximationinthenisqcontext AT springerfachmedienwiesbaden reinforcementlearningwithhybridquantumapproximationinthenisqcontext |