Quantum machine learning and optimisation in finance: on the road to quantum advantage
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| Format: | Buch |
| Sprache: | English |
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Birmingham ; Mumbai
Packt
[October 2022]
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| Ausgabe: | First published |
| Schriftenreihe: | Expert insight
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xlii, 396 Seiten Illustrationen, Diagramme |
| ISBN: | 9781801813570 |
Internformat
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| 653 | 0 | |a Finance / Mathematical models | |
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| 653 | 0 | |a Quantum computing | |
| 653 | 0 | |a Finance / Data processing | |
| 653 | 0 | |a Finance / Mathematical models | |
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Datensatz im Suchindex
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TabÍe of Contents Preface xxxiii Chapter 1: The Principles of Quantum Mechanics 1 1.1 Linear Algebra for Quantum Mechanics . 2 1.1.1 Basic definitions and notations · 2 1.1.2 Inner products · 3 1.1.3 From linear operators to matrices · 4 1.1.4 Condition number · 6 1.1.5 Matrix decompositions and spectral theorem · 8 1.1.6 Hermitian matrices · 9 1.1.7 Rotation matrices · 11 1.1.8 Polar coordinates »12 1.1.9 Dirac notations · 14 1.1.10 Quantum operators · 15 1.1.11 Tensor product »16 1.2 Postulates of Quantum Mechanics . 1.2.1 First postulate - Statics · 17 1.2.2 Second postulate - Dynamics · 19 1.2.3 Third postulate - Measurement · 21 1.2.4 Fourth postulate - Observable · 24 1.2.5 Fifth postulate - Composite System · 25 16
1.3 Pure and Mixed States . 26 1.3.1 Density matrix · 26 1.3.2 Pure state · 28 1.3.3 Mixed state · 29 Part I : Analog Quantum Computing - Quantum Annealing 35 Chapter 2: Adiabatic Quantum Computing 37 2.1 Complexity of Computational Problems . 38 2.2 Principles of Adiabatic Quantum Computing . 40 2.2.1 The Quantum Adiabatic Theorem · 43 2.2.2 Optimisation and metaheuristics · 46 Simulated annealing · 47 Quantum annealing and quantum tunnelling · 47 2.3 Implementations of AQC . 50 2.3.1 The short history of quantum annealing · 50 2.3.2 Inter-generational comparison of D-Wave quantum annealers · 51 2.3.3 Physical realisations of quantum annealers · 53 2.3.4 Chimera graph and embedding of the logical qubits · 55 2.4 Universality of AQC . 57 Chapter 3: Quadratic Unconstrained Binary Optimisation 61 3.1 Principles of Quadratic Unconstrained Binary Optimisation . 62 3.1.1 QUBO to Ising transformation · 63 3.1.2 QUBO problem examples · 63 Number Partitioning · 63 Graph Partitioning · 64 Binary Integer Linear Programming · 65 Knapsack with Integer Weights · 65
3.2 Forward and Reverse Quantum Annealing . 67 3.2.1 Forward quantum annealing · 67 3.2.2 Reverse quantum annealing · 68 3.3 Discrete Portfolio Optimisation . 70 3.3.1 QUBO encoding · 71 3.3.2 The coarse-grained encoding scheme · 72 3.3.3 Construction of the instance set for numerical experiments · 73 3.3.4 Classical benchmark - Genetic Algorithm · 74 3.3.5 Establishing quantum speedup · 79 Chapter 4: Quantum Boosting 83 4.1 Quantum Annealing for Machine Learning . 84 4.1.1 General principles of the QBoost algorithm · 85 4.1.2 QUBO to Ising · 86 4.2 QBoost Applications in Finance . 87 4.2.1 Credit card defaults · 88 4.2.2 QUBO classification results · 90 4.3 Classical Benchmarks . 93 4.3.1 Artificial neural network · 93 4.3.2 Training artificial neural networks · 95 4.3.3 Decision trees and gradient boosting · 98 4.3.4 Benchmarking against standard classical classifiers · 100 Chapter 5: Quantum Boltzmann Machine 105 5.1 From Graph Theory to Boltzmann Machines . 106 5.2 Restricted Boltzmann Machine . 108 5.2.1 The RBM as an energy-based model · 108 5.2.2 RBM network architecture · 111 5.2.3 Sample encoding · 113 5.2.4 Boltzmann
distribution · 113
5.2.5 Extensions of the Bernoulli RBM · 114 5.3 Training and Running RBM . 116 5.3.1 Training RBM with Boltzmann sampling «116 5.3.2 The Contrastive Divergence algorithm · 116 5.3.3 Generation of synthetic samples · 118 5.4 Quantum Annealing and Boltzmann Sampling . 122 5.4.1 Boltzmann sampling · 123 5.4.2 Mapping · 125 5.4.3 Hardware embedding and parameters optimisation · 126 5.4.4 Generative models · 129 5.5 Deep Boltzmann Machine . 130 5.5.1 Training DBMs with quantum annealing · 131 5.5.2 A DBM pipeline example · 132 Part II: Gate Model· Quantum Computing 135 Chapter 6: Qubits and Quantum Logic Gates 137 6.1 Binary Digit (Bit) and Logic Gates . 138 6.1.1 Logic gates · 138 6.1.2 NAND as a universal logic gate · 139 6.1.3 Building an addition operator from the NAND gates · 140 6.2 Physical Realisations of Classical Bits and Logic Gates . 142 6.2.1 Implementation of the NAND gate · 142 6.2.2 Implementation of the RAM memory cell · 144 6.3 Quantum Binary Digit (Qubit) and Quantum Logic Gates . 145 6.3.1 Computation according to the laws of quantum mechanics · 145 6.3.2 Qubit · 148 6.3.3 One-qubit quantum logic gates · 150 6.3.4 Two-qubit quantum logic gates · 153 6.3.5 The Toffoli gate · 156
6.4 Reversible Computing . 159 6.5 Entanglement . 161 6.5.1 Quantum entanglement and why it matters · 161 6.5.2 Entangling qubit states with two-qubit gates · 163 6.6 Quantum Gate Decompositions . 164 6.7 Physical Realisations of Qubits and Quantum Gates . 168 6.7.1 The DiVincenzo criteria · 168 6.7.2 Superconducting qubits · 170 From classical to quantum harmonic oscillator · 170 Physical representation of the QHO « 174 Controlling and measuring superconducting qubits · 177 Entanglement -with superconducting qubits » 178 6.7.3 Photonic qubits »179 6.7.4 Trapped ion qubits · 181 6.8 Quantum Hardware and Simulators . 184 Chapter 7: Parameterised Quantum Circuits and Data Encoding 189 7.1 Parameterised Quantum Circuits . 190 7.2 Angle Encoding . 193 7.2.1 The basic encoding scheme · 193 7.2.2 Encoding two features per quantum register · 195 7.2.3 Mapping a classical data sample into a quantum state · 196 7.3 Amplitude Encoding . 196 7.4 Binary Inputs into Basis States
. 198 7.5 Superposition Encoding . 199 7.6 Hamiltonian Simulation . 202 Chapter 8: Quantum Neural Network 207 8.1 Quantum Neural Networks . 208
8.2 Training QNN with Gradient Descent . 211 8.2.1 The finite difference scheme · 211 8.2.2 The analytic gradient approach · 213 8.2.3 The parameter shift rule for analytic gradient calculation · 214 8.3 Training QNN with Particle Swarm Optimisation . 217 8.3.1 The Particle Swarm Optimisation algorithm · 217 8.3.2 PSO algorithm for training quantum neural networks · 219 8.4 QNN Embedding on NISQ QPU . 224 8.4.1 NISQ QPU connectivity · 224 8.4.2 QNN embedding scheme · 225 8.5 QNN Trained as a Classifier . 226 8.5.1 The АСА dataset and QNN ansatz · 226 8.5.2 Training an АСА classifier with the PSO algorithm · 227 8.6 Classical Benchmarks . 229 8.6.1 Logistic Regression and Random Forest · 229 8.6.2 Benchmarking against standard classical classifiers · 230 8.7 Improving Performance with Ensemble Learning . 231 8.7.1 Majority voting · 232 8.7.2 Quantum boosting · 235 Chapter 9: Quantum Circuit Born Machine 239 9.1 Constructing QCBM . 240 9.1.1 QCBM architecture · 240 9.1.2 QCBM embedding · 242 9.2 Differentiable Learning of QCBM . 244 9.2.1 Sample
encoding · 244 9.2.2 Choosing the right cost function · 247 9.3 Non-Differentiable Learning of QCBM . 249 9.3.1 The principles of Genetic Algorithm · 249 9.3.2 Training QCBM with a Genetic Algorithm · 250
9.4 Classical Benchmark . 254 9.5 QCBM as a Market Generator . 256 9.5.1 Non-parametric modelling of market risk factors · 256 9.5.2 Sampling from the learned probability distributions · 257 9.5.3 Training algorithm convergence and hyperparameter optimisation · 263 Chapter 10: Variational Quantum Eigensolver 269 10.1 The Variational Approach . 270 10.2 Calculating Expectations on a Quantum Computer . 272 10.2.1 The one-qubit case · 272 10.2.2 The two-qubit case · 276 10.2.3 The multi-qubit case · 278 10.3 Constructing the PQC . 279 10.3.1 One-qubit ansatz · 280 10.3.2 Multi-qubit ansatz · 281 10.4 Running the PQC . 283 10.4.1 Experimenting with the two-qubit ansatz · 283 10.4.2 Analysis of the obtained results · 285 10.5 Discrete Portfolio Optimisation with VQE . 288 Chapter 11: Quantum Approximate Optimisation Algorithm 293 11.1 Time Evolution . 294 11.2 The Suzuki-Trotter Expansion . 296 11.3 The Algorithm
Specification . 298 11.4 The Max-Cut Problem . 299 11.4.1 QAOA gates · 301 11.4.2 QAOA circuit · 305 Chapter 12: The Power of Parameterised Quantum Circuits 309
12.1 Strong Régularisation . 310 12.1.1 Lipschitz constant · 311 12.1.2 Régularisation example · 312 12.2 Expressive Power . 315 12.2.1 Multilayer PQC · 316 12.2.2 Tensor network PQC · 317 12.2.3 Measures of expressive power · 318 12.2.4 Expressive power of PQC · 322 Chapter 13: Looking Ahead 325 13.1 Quantum Kernels . 326 13.1.1 Classical kernel method · 326 13.1.2 Quantum kernel method · 327 13.1.3 Quantum circuits for the feature maps · 328 13.2 Quantum Generative Adversarial Networks . 331 13.3 Bayesian Quantum Circuit . 335 13.4 Quantum Semidefinite Programming . 337 13.4.1 Classical semidefinite programming · 338 13.4.2 Maximum risk analysis · 338 13.4.3 Robust portfolio construction · 339 13.4.4 Quantum semidefinite programming · 340 13.5 Beyond NISQ . 342 13.5.1 Quantum Fourier Transform · 342 13.5.2 Quantum Phase Estimation · 343 13.5.3 Monte Carlo speedup · 344 Classical Monte Carlo · 345 Quantum Monte Carlo · 345 QMC speedup · 347 13.5.4 Quantum Linear Solver · 349 Theoretical aspects · 349
Solving PDEs · 352 Application to portfolio optimisation · 355 Bibliography 357 Index 386 Other Books You Might Enjoy 393 |
| adam_txt |
TabÍe of Contents Preface xxxiii Chapter 1: The Principles of Quantum Mechanics 1 1.1 Linear Algebra for Quantum Mechanics . 2 1.1.1 Basic definitions and notations · 2 1.1.2 Inner products · 3 1.1.3 From linear operators to matrices · 4 1.1.4 Condition number · 6 1.1.5 Matrix decompositions and spectral theorem · 8 1.1.6 Hermitian matrices · 9 1.1.7 Rotation matrices · 11 1.1.8 Polar coordinates »12 1.1.9 Dirac notations · 14 1.1.10 Quantum operators · 15 1.1.11 Tensor product »16 1.2 Postulates of Quantum Mechanics . 1.2.1 First postulate - Statics · 17 1.2.2 Second postulate - Dynamics · 19 1.2.3 Third postulate - Measurement · 21 1.2.4 Fourth postulate - Observable · 24 1.2.5 Fifth postulate - Composite System · 25 16
1.3 Pure and Mixed States . 26 1.3.1 Density matrix · 26 1.3.2 Pure state · 28 1.3.3 Mixed state · 29 Part I : Analog Quantum Computing - Quantum Annealing 35 Chapter 2: Adiabatic Quantum Computing 37 2.1 Complexity of Computational Problems . 38 2.2 Principles of Adiabatic Quantum Computing . 40 2.2.1 The Quantum Adiabatic Theorem · 43 2.2.2 Optimisation and metaheuristics · 46 Simulated annealing · 47 Quantum annealing and quantum tunnelling · 47 2.3 Implementations of AQC . 50 2.3.1 The short history of quantum annealing · 50 2.3.2 Inter-generational comparison of D-Wave quantum annealers · 51 2.3.3 Physical realisations of quantum annealers · 53 2.3.4 Chimera graph and embedding of the logical qubits · 55 2.4 Universality of AQC . 57 Chapter 3: Quadratic Unconstrained Binary Optimisation 61 3.1 Principles of Quadratic Unconstrained Binary Optimisation . 62 3.1.1 QUBO to Ising transformation · 63 3.1.2 QUBO problem examples · 63 Number Partitioning · 63 Graph Partitioning · 64 Binary Integer Linear Programming · 65 Knapsack with Integer Weights · 65
3.2 Forward and Reverse Quantum Annealing . 67 3.2.1 Forward quantum annealing · 67 3.2.2 Reverse quantum annealing · 68 3.3 Discrete Portfolio Optimisation . 70 3.3.1 QUBO encoding · 71 3.3.2 The coarse-grained encoding scheme · 72 3.3.3 Construction of the instance set for numerical experiments · 73 3.3.4 Classical benchmark - Genetic Algorithm · 74 3.3.5 Establishing quantum speedup · 79 Chapter 4: Quantum Boosting 83 4.1 Quantum Annealing for Machine Learning . 84 4.1.1 General principles of the QBoost algorithm · 85 4.1.2 QUBO to Ising · 86 4.2 QBoost Applications in Finance . 87 4.2.1 Credit card defaults · 88 4.2.2 QUBO classification results · 90 4.3 Classical Benchmarks . 93 4.3.1 Artificial neural network · 93 4.3.2 Training artificial neural networks · 95 4.3.3 Decision trees and gradient boosting · 98 4.3.4 Benchmarking against standard classical classifiers · 100 Chapter 5: Quantum Boltzmann Machine 105 5.1 From Graph Theory to Boltzmann Machines . 106 5.2 Restricted Boltzmann Machine . 108 5.2.1 The RBM as an energy-based model · 108 5.2.2 RBM network architecture · 111 5.2.3 Sample encoding · 113 5.2.4 Boltzmann
distribution · 113
5.2.5 Extensions of the Bernoulli RBM · 114 5.3 Training and Running RBM . 116 5.3.1 Training RBM with Boltzmann sampling «116 5.3.2 The Contrastive Divergence algorithm · 116 5.3.3 Generation of synthetic samples · 118 5.4 Quantum Annealing and Boltzmann Sampling . 122 5.4.1 Boltzmann sampling · 123 5.4.2 Mapping · 125 5.4.3 Hardware embedding and parameters optimisation · 126 5.4.4 Generative models · 129 5.5 Deep Boltzmann Machine . 130 5.5.1 Training DBMs with quantum annealing · 131 5.5.2 A DBM pipeline example · 132 Part II: Gate Model· Quantum Computing 135 Chapter 6: Qubits and Quantum Logic Gates 137 6.1 Binary Digit (Bit) and Logic Gates . 138 6.1.1 Logic gates · 138 6.1.2 NAND as a universal logic gate · 139 6.1.3 Building an addition operator from the NAND gates · 140 6.2 Physical Realisations of Classical Bits and Logic Gates . 142 6.2.1 Implementation of the NAND gate · 142 6.2.2 Implementation of the RAM memory cell · 144 6.3 Quantum Binary Digit (Qubit) and Quantum Logic Gates . 145 6.3.1 Computation according to the laws of quantum mechanics · 145 6.3.2 Qubit · 148 6.3.3 One-qubit quantum logic gates · 150 6.3.4 Two-qubit quantum logic gates · 153 6.3.5 The Toffoli gate · 156
6.4 Reversible Computing . 159 6.5 Entanglement . 161 6.5.1 Quantum entanglement and why it matters · 161 6.5.2 Entangling qubit states with two-qubit gates · 163 6.6 Quantum Gate Decompositions . 164 6.7 Physical Realisations of Qubits and Quantum Gates . 168 6.7.1 The DiVincenzo criteria · 168 6.7.2 Superconducting qubits · 170 From classical to quantum harmonic oscillator · 170 Physical representation of the QHO « 174 Controlling and measuring superconducting qubits · 177 Entanglement -with superconducting qubits » 178 6.7.3 Photonic qubits »179 6.7.4 Trapped ion qubits · 181 6.8 Quantum Hardware and Simulators . 184 Chapter 7: Parameterised Quantum Circuits and Data Encoding 189 7.1 Parameterised Quantum Circuits . 190 7.2 Angle Encoding . 193 7.2.1 The basic encoding scheme · 193 7.2.2 Encoding two features per quantum register · 195 7.2.3 Mapping a classical data sample into a quantum state · 196 7.3 Amplitude Encoding . 196 7.4 Binary Inputs into Basis States
. 198 7.5 Superposition Encoding . 199 7.6 Hamiltonian Simulation . 202 Chapter 8: Quantum Neural Network 207 8.1 Quantum Neural Networks . 208
8.2 Training QNN with Gradient Descent . 211 8.2.1 The finite difference scheme · 211 8.2.2 The analytic gradient approach · 213 8.2.3 The parameter shift rule for analytic gradient calculation · 214 8.3 Training QNN with Particle Swarm Optimisation . 217 8.3.1 The Particle Swarm Optimisation algorithm · 217 8.3.2 PSO algorithm for training quantum neural networks · 219 8.4 QNN Embedding on NISQ QPU . 224 8.4.1 NISQ QPU connectivity · 224 8.4.2 QNN embedding scheme · 225 8.5 QNN Trained as a Classifier . 226 8.5.1 The АСА dataset and QNN ansatz · 226 8.5.2 Training an АСА classifier with the PSO algorithm · 227 8.6 Classical Benchmarks . 229 8.6.1 Logistic Regression and Random Forest · 229 8.6.2 Benchmarking against standard classical classifiers · 230 8.7 Improving Performance with Ensemble Learning . 231 8.7.1 Majority voting · 232 8.7.2 Quantum boosting · 235 Chapter 9: Quantum Circuit Born Machine 239 9.1 Constructing QCBM . 240 9.1.1 QCBM architecture · 240 9.1.2 QCBM embedding · 242 9.2 Differentiable Learning of QCBM . 244 9.2.1 Sample
encoding · 244 9.2.2 Choosing the right cost function · 247 9.3 Non-Differentiable Learning of QCBM . 249 9.3.1 The principles of Genetic Algorithm · 249 9.3.2 Training QCBM with a Genetic Algorithm · 250
9.4 Classical Benchmark . 254 9.5 QCBM as a Market Generator . 256 9.5.1 Non-parametric modelling of market risk factors · 256 9.5.2 Sampling from the learned probability distributions · 257 9.5.3 Training algorithm convergence and hyperparameter optimisation · 263 Chapter 10: Variational Quantum Eigensolver 269 10.1 The Variational Approach . 270 10.2 Calculating Expectations on a Quantum Computer . 272 10.2.1 The one-qubit case · 272 10.2.2 The two-qubit case · 276 10.2.3 The multi-qubit case · 278 10.3 Constructing the PQC . 279 10.3.1 One-qubit ansatz · 280 10.3.2 Multi-qubit ansatz · 281 10.4 Running the PQC . 283 10.4.1 Experimenting with the two-qubit ansatz · 283 10.4.2 Analysis of the obtained results · 285 10.5 Discrete Portfolio Optimisation with VQE . 288 Chapter 11: Quantum Approximate Optimisation Algorithm 293 11.1 Time Evolution . 294 11.2 The Suzuki-Trotter Expansion . 296 11.3 The Algorithm
Specification . 298 11.4 The Max-Cut Problem . 299 11.4.1 QAOA gates · 301 11.4.2 QAOA circuit · 305 Chapter 12: The Power of Parameterised Quantum Circuits 309
12.1 Strong Régularisation . 310 12.1.1 Lipschitz constant · 311 12.1.2 Régularisation example · 312 12.2 Expressive Power . 315 12.2.1 Multilayer PQC · 316 12.2.2 Tensor network PQC · 317 12.2.3 Measures of expressive power · 318 12.2.4 Expressive power of PQC · 322 Chapter 13: Looking Ahead 325 13.1 Quantum Kernels . 326 13.1.1 Classical kernel method · 326 13.1.2 Quantum kernel method · 327 13.1.3 Quantum circuits for the feature maps · 328 13.2 Quantum Generative Adversarial Networks . 331 13.3 Bayesian Quantum Circuit . 335 13.4 Quantum Semidefinite Programming . 337 13.4.1 Classical semidefinite programming · 338 13.4.2 Maximum risk analysis · 338 13.4.3 Robust portfolio construction · 339 13.4.4 Quantum semidefinite programming · 340 13.5 Beyond NISQ . 342 13.5.1 Quantum Fourier Transform · 342 13.5.2 Quantum Phase Estimation · 343 13.5.3 Monte Carlo speedup · 344 Classical Monte Carlo · 345 Quantum Monte Carlo · 345 QMC speedup · 347 13.5.4 Quantum Linear Solver · 349 Theoretical aspects · 349
Solving PDEs · 352 Application to portfolio optimisation · 355 Bibliography 357 Index 386 Other Books You Might Enjoy 393 |
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| author | Jacquier, Antoine Kondratyev, Oleksiy ca. 20./21. Jh |
| author_GND | (DE-588)1293423963 (DE-588)1293424366 |
| author_facet | Jacquier, Antoine Kondratyev, Oleksiy ca. 20./21. Jh |
| author_role | aut aut |
| author_sort | Jacquier, Antoine |
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| building | Verbundindex |
| bvnumber | BV048868994 |
| classification_rvk | ST 152 |
| ctrlnum | (OCoLC)1385299292 (DE-599)BVBBV048868994 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First published |
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| id | DE-604.BV048868994 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:43:47Z |
| indexdate | 2024-11-19T09:00:46Z |
| institution | BVB |
| isbn | 9781801813570 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034133948 |
| oclc_num | 1385299292 |
| open_access_boolean | |
| owner | DE-739 DE-11 |
| owner_facet | DE-739 DE-11 |
| physical | xlii, 396 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Packt |
| record_format | marc |
| series2 | Expert insight |
| spelling | Jacquier, Antoine Verfasser (DE-588)1293423963 aut Quantum machine learning and optimisation in finance on the road to quantum advantage First published Birmingham ; Mumbai Packt [October 2022] © 2022 xlii, 396 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Expert insight Quanteninformation (DE-588)1211521885 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Quantencomputer (DE-588)4533372-5 gnd rswk-swf Quanteninformatik (DE-588)4705961-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Finance / Data processing Finance / Mathematical models Machine learning Quantum computing Maschinelles Lernen (DE-588)4193754-5 s Künstliche Intelligenz (DE-588)4033447-8 s Quanteninformation (DE-588)1211521885 s Quantencomputer (DE-588)4533372-5 s Quanteninformatik (DE-588)4705961-8 s DE-604 Kondratyev, Oleksiy ca. 20./21. Jh. Verfasser (DE-588)1293424366 aut Erscheint auch als Online-Ausgabe 978-1-80181-787-5 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034133948&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Jacquier, Antoine Kondratyev, Oleksiy ca. 20./21. Jh Quantum machine learning and optimisation in finance on the road to quantum advantage Quanteninformation (DE-588)1211521885 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Quantencomputer (DE-588)4533372-5 gnd Quanteninformatik (DE-588)4705961-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1211521885 (DE-588)4033447-8 (DE-588)4533372-5 (DE-588)4705961-8 (DE-588)4193754-5 |
| title | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_auth | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_exact_search | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_exact_search_txtP | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_full | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_fullStr | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_full_unstemmed | Quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_short | Quantum machine learning and optimisation in finance |
| title_sort | quantum machine learning and optimisation in finance on the road to quantum advantage |
| title_sub | on the road to quantum advantage |
| topic | Quanteninformation (DE-588)1211521885 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Quantencomputer (DE-588)4533372-5 gnd Quanteninformatik (DE-588)4705961-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Quanteninformation Künstliche Intelligenz Quantencomputer Quanteninformatik Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034133948&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jacquierantoine quantummachinelearningandoptimisationinfinanceontheroadtoquantumadvantage AT kondratyevoleksiy quantummachinelearningandoptimisationinfinanceontheroadtoquantumadvantage |