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Exploring quantum active learning for materials design and discovery.

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Quantum active learning (QAL) enhances materials discovery by integrating quantum machine learning (QML) algorithms. This approach shows potential for optimizing searches in materials science and chemistry, especially with limited data.

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Area of Science:

  • Materials Science
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Classical active learning (AL) has demonstrated efficiency in data usage for materials discovery.
  • Quantum machine learning (QML) offers potential for developing advanced regression models.
  • The synergy between AI and quantum computing is a rapidly advancing field.

Purpose of the Study:

  • To explore the application of quantum algorithms within the active learning (AL) framework for materials discovery.
  • To investigate the performance of quantum active learning (QAL) using quantum support vector regressors (QSVR) and quantum Gaussian process regressors (QGPR).
  • To compare QAL performance against classical AL in materials property prediction and structure optimization.

Main Methods:

  • Implementation of QAL using MLChem4D and QMLMaterial codes.
  • Utilization of QSVR and QGPR with diverse quantum kernels and feature maps.
  • Application to datasets including perovskite properties (piezoelectric coefficient, band gap, energy storage) and nanoparticle structure optimization via density functional theory.

Main Results:

  • QAL improved search efficiency in most tested cases.
  • Performance variations in QAL were observed, potentially linked to data "roughness."
  • The study validated the potential of QAL for discovering optimal solutions within chemical spaces.

Conclusions:

  • QAL represents a promising advancement for data-efficient materials discovery.
  • The integration of quantum chemistry with QML, termed the "QQ method," facilitates new inferences and discoveries.
  • QAL holds significant potential for applications in materials science, chemistry, and beyond.