A divide-and-conquer algorithm for quantum state preparation
Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum...
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| Veröffentlicht in: | Scientific reports Jg. 11; H. 1; S. 6329 - 12 |
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| Abstract | Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth
O
(
N
) to load an
N
-dimensional vector. Here, we show that it is possible to load an
N
-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices. |
|---|---|
| AbstractList | Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices. Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices.Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices. Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O ( N ) to load an N -dimensional vector. Here, we show that it is possible to load an N -dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices. Abstract Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data in quantum computers can impose restrictions on possible quantum speedups. Known algorithms to create arbitrary quantum states require quantum circuits with depth O(N) to load an N-dimensional vector. Here, we show that it is possible to load an N-dimensional vector with exponential time advantage using a quantum circuit with polylogarithmic depth and entangled information in ancillary qubits. Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space. We demonstrate a proof of concept on a real quantum device and present two applications for quantum machine learning. We expect that this new loading strategy allows the quantum speedup of tasks that require to load a significant volume of information to quantum devices. |
| ArticleNumber | 6329 |
| Author | Araujo, Israel F. da Silva, Adenilton J. Park, Daniel K. Petruccione, Francesco |
| Author_xml | – sequence: 1 givenname: Israel F. surname: Araujo fullname: Araujo, Israel F. organization: Centro de Informática, Universidade Federal de Pernambuco – sequence: 2 givenname: Daniel K. surname: Park fullname: Park, Daniel K. organization: Sungkyunkwan University Advanced Institute of Nanotechnology – sequence: 3 givenname: Francesco surname: Petruccione fullname: Petruccione, Francesco email: petruccione@ukzn.ac.za organization: School of Electrical Engineering, KAIST, Quantum Research Group, School of Chemistry and Physics, University of KwaZulu-Natal, National Institute for Theoretical Physics (NITheP) – sequence: 4 givenname: Adenilton J. surname: da Silva fullname: da Silva, Adenilton J. organization: Centro de Informática, Universidade Federal de Pernambuco |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33737544$$D View this record in MEDLINE/PubMed |
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| Snippet | Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load classical data... Abstract Advantages in several fields of research and industry are expected with the rise of quantum computers. However, the computational cost to load... |
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| SubjectTerms | 639/705/117 639/705/258 639/766/483/2802 639/766/483/481 Algorithms Computer applications Computers Humanities and Social Sciences Learning algorithms Machine learning multidisciplinary Quantum computing Science Science (multidisciplinary) |
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| Title | A divide-and-conquer algorithm for quantum state preparation |
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