On quantum methods for machine learning problems part I: Quantum tools
| Autor: | Kamil Khadiev, Dingming Wu, Farid Ablayev, Joshua Zhexue Huang, Nailya R. Salikhova, Marat Ablayev |
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| Rok vydání: | 2020 |
| Předmět: |
Quantum programming
Computer Networks and Communications business.industry Computer science Subroutine TheoryofComputation_GENERAL Machine learning computer.software_genre Quantum search Computer Science Applications Statistical classification Artificial Intelligence Quantum state ComputerSystemsOrganization_MISCELLANEOUS Qubit Quantum algorithm Artificial intelligence business computer Quantum Information Systems computer.programming_language |
| Zdroj: | Big Data Mining and Analytics. 3:41-55 |
| ISSN: | 2096-0654 |
| DOI: | 10.26599/bdma.2019.9020016 |
| Popis: | This is a review of quantum methods for machine learning problems that consists of two parts. The first part, “quantum tools”, presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, “quantum classification algorithms”, introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification. |
| Databáze: | OpenAIRE |
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