Reinforcement learning with hybrid quantum approximation in the NISQ context:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Wiesbaden, Germany
Springer Vieweg
[2022]
|
| Schriftenreihe: | Research
Moremedia |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 127-134 |
| Beschreibung: | xviii, 134 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 211 g |
| ISBN: | 9783658376154 3658376155 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048316757 | ||
| 003 | DE-604 | ||
| 005 | 20220829 | ||
| 007 | t | ||
| 008 | 220706s2022 gw a||| m||| 00||| eng d | ||
| 015 | |a 22,N13 |2 dnb | ||
| 016 | 7 | |a 1254160140 |2 DE-101 | |
| 020 | |a 9783658376154 |c Festeinband : circa EUR 80.24 (DE) (freier Preis), circa EUR 82.49 (AT) (freier Preis), circa CHF 88.50 (freier Preis), circa EUR 74.99 |9 978-3-658-37615-4 | ||
| 020 | |a 3658376155 |9 3-658-37615-5 | ||
| 024 | 3 | |a 9783658376154 | |
| 028 | 5 | 2 | |a Bestellnummer: 978-3-658-37615-4 |
| 028 | 5 | 2 | |a Bestellnummer: 89185087 |
| 035 | |a (OCoLC)1335403405 | ||
| 035 | |a (DE-599)DNB1254160140 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-HE | ||
| 049 | |a DE-706 |a DE-83 | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a ST 152 |0 (DE-625)143596: |2 rvk | ||
| 084 | |8 1\p |a 004 |2 23sdnb | ||
| 100 | 1 | |a Kunczik, Leonhard |e Verfasser |0 (DE-588)1261934989 |4 aut | |
| 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 | |
| 300 | |a xviii, 134 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 211 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Research | |
| 490 | 0 | |a Moremedia | |
| 500 | |a Literaturverzeichnis Seite 127-134 | ||
| 502 | |b Dissertation |c Universität der Bundeswehr München, Neubiberg |d 2021 | ||
| 650 | 0 | 7 | |a Computersicherheit |0 (DE-588)4274324-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Quantencomputer |0 (DE-588)4533372-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bestärkendes Lernen |g Künstliche Intelligenz |0 (DE-588)4825546-4 |2 gnd |9 rswk-swf |
| 653 | |a Quantum Machine Learning | ||
| 653 | |a Quantum Reinforcement Learning | ||
| 653 | |a Quanten Computing | ||
| 653 | |a Reinforcement Learning | ||
| 653 | |a Attacker-Defender Scenarios | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Bestärkendes Lernen |g Künstliche Intelligenz |0 (DE-588)4825546-4 |D s |
| 689 | 0 | 1 | |a Quantencomputer |0 (DE-588)4533372-5 |D s |
| 689 | 0 | 2 | |a Computersicherheit |0 (DE-588)4274324-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 710 | 2 | |a Springer Fachmedien Wiesbaden |0 (DE-588)1043386068 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-658-37616-1 |
| 856 | 4 | 2 | |m X:MVB |q text/html |u http://deposit.dnb.de/cgi-bin/dokserv?id=9aa5f6f2f7a44362a77ddcce355720f0&prov=M&dok_var=1&dok_ext=htm |3 Inhaltstext |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u 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 |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-033696216 | ||
| 883 | 1 | |8 1\p |a vlb |d 20220327 |q DE-101 |u https://d-nb.info/provenance/plan#vlb | |
Datensatz im Suchindex
| _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 |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02899nam a2200649 c 4500</leader><controlfield tag="001">BV048316757</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220829 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">220706s2022 gw a||| m||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">22,N13</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1254160140</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783658376154</subfield><subfield code="c">Festeinband : circa EUR 80.24 (DE) (freier Preis), circa EUR 82.49 (AT) (freier Preis), circa CHF 88.50 (freier Preis), circa EUR 74.99</subfield><subfield code="9">978-3-658-37615-4</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3658376155</subfield><subfield code="9">3-658-37615-5</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9783658376154</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Bestellnummer: 978-3-658-37615-4</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">Bestellnummer: 89185087</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1335403405</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1254160140</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-HE</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-706</subfield><subfield code="a">DE-83</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 152</subfield><subfield code="0">(DE-625)143596:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="8">1\p</subfield><subfield code="a">004</subfield><subfield code="2">23sdnb</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kunczik, Leonhard</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1261934989</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Reinforcement learning with hybrid quantum approximation in the NISQ context</subfield><subfield code="c">Leonhard Kunczik</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Wiesbaden, Germany</subfield><subfield code="b">Springer Vieweg</subfield><subfield code="c">[2022]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2022</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xviii, 134 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">21 cm x 14.8 cm, 211 g</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Research</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Moremedia</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Literaturverzeichnis Seite 127-134</subfield></datafield><datafield tag="502" ind1=" " ind2=" "><subfield code="b">Dissertation</subfield><subfield code="c">Universität der Bundeswehr München, Neubiberg</subfield><subfield code="d">2021</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Computersicherheit</subfield><subfield code="0">(DE-588)4274324-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Quantencomputer</subfield><subfield code="0">(DE-588)4533372-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bestärkendes Lernen</subfield><subfield code="g">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4825546-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Quantum Machine Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Quantum Reinforcement Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Quanten Computing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Reinforcement Learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Attacker-Defender Scenarios</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4113937-9</subfield><subfield code="a">Hochschulschrift</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Bestärkendes Lernen</subfield><subfield code="g">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4825546-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Quantencomputer</subfield><subfield code="0">(DE-588)4533372-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Computersicherheit</subfield><subfield code="0">(DE-588)4274324-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Springer Fachmedien Wiesbaden</subfield><subfield code="0">(DE-588)1043386068</subfield><subfield code="4">pbl</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-3-658-37616-1</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">X:MVB</subfield><subfield code="q">text/html</subfield><subfield code="u">http://deposit.dnb.de/cgi-bin/dokserv?id=9aa5f6f2f7a44362a77ddcce355720f0&prov=M&dok_var=1&dok_ext=htm</subfield><subfield code="3">Inhaltstext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">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</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033696216</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">vlb</subfield><subfield code="d">20220327</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#vlb</subfield></datafield></record></collection> |
| 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 |