| Download | - View final version: Quantum active learning for structural determination of doped nanoparticles: a case study of 4Al@Si₁₁ (PDF, 1.6 MiB)
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| DOI | Resolve DOI: https://doi.org/10.21577/0103-5053.20250054 |
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| Author | Search for: Pierre Lourenço, MaiconORCID identifier: https://orcid.org/0000-0002-0110-8318; Search for: Naseri, Mosayeb1ORCID identifier: https://orcid.org/0000-0003-0786-2175; Search for: Barrios Herrera, Lizandra; Search for: Zadeh-Haghighi, HadiORCID identifier: https://orcid.org/0000-0003-3380-9925; Search for: Gaur, Daya; Search for: Simon, Christoph; Search for: Salahub, Dennis R.ORCID identifier: https://orcid.org/0000-0002-9848-3762 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | quantum computing; quantum machine learning; quantum active learning; doped materials |
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| Abstract | Active learning (AL) has been widely applied in chemistry and materials science. In this work, we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by Density Functional Theory (DFT) or Density Functional Based Tight Binding (DFTB) and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si₁₁ with 4 Al (4Al@Si₁₁) and the results indicate the QAL method is able to find the optimum 4Al@Si₁₁ structure. The aim of this work is to present the QAL method, formulated in a noise-free quantum computing framework, for automatic structural determination of doped nanoparticles and materials defects. |
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| Publication date | 2025-03-31 |
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| Publisher | Sociedade Brasileira de Quimica |
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| Licence | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c9dee348-c420-4167-add1-df1bc05b40ed |
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| Record created | 2025-06-23 |
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| Record modified | 2025-06-24 |
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