Quantum Machine Learning algorithms
GPTKB entity
Statements (52)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
|
| gptkbp:advantage |
potential exponential speedup
|
| gptkbp:appliesTo |
quantum computers
classical data |
| gptkbp:category |
gptkb:reinforcement_learning
supervised learning generative models unsupervised learning hybrid quantum-classical algorithms |
| gptkbp:challenge |
decoherence
error rates data encoding bottleneck limited qubit count noisy intermediate-scale quantum devices |
| gptkbp:enables |
quantum speedup
|
| gptkbp:field |
Quantum Machine Learning
|
| gptkbp:includes |
gptkb:Variational_Quantum_Eigensolver
Quantum Boltzmann Machine Quantum Clustering Quantum Generative Adversarial Network Quantum Neural Networks Quantum Principal Component Analysis Quantum Reinforcement Learning Quantum Support Vector Machine Quantum k-Means |
| gptkbp:notableContributor |
gptkb:Seth_Lloyd
Maria Schuld Nathan Wiebe Peter Wittek |
| gptkbp:proposedBy |
early 2000s
|
| gptkbp:publishedIn |
gptkb:Nature
gptkb:Physical_Review_Letters gptkb:arXiv |
| gptkbp:relatedTo |
gptkb:machine_learning
gptkb:quantum_computing |
| gptkbp:requires |
quantum hardware
quantum circuits quantum gates quantum data encoding |
| gptkbp:studiedBy |
gptkb:Google
gptkb:IBM gptkb:Microsoft academic institutions startups |
| gptkbp:usedFor |
gptkb:dictionary
optimization regression clustering dimensionality reduction |
| gptkbp:bfsParent |
gptkb:Noisy_Intermediate-Scale_Quantum_(NISQ)_algorithm
|
| gptkbp:bfsLayer |
7
|
| https://www.w3.org/2000/01/rdf-schema#label |
Quantum Machine Learning algorithms
|