Quantum classifiers for domain adaptation

The article presents two quantum implementations of domain adaptation classifiers: one based on quantum basic linear algebra subroutines (QBLAS) and another using a variational hybrid quantum-classical procedure. The QBLAS-based classifier offers exponential speedup on universal quantum computers, while the variational quantum domain adaptation classifier (VQDAC) is designed for near-term quantum devices. Both implementations aim to reduce computational complexity and improve performance in machine learning tasks involving domain adaptation. Numerical experiments demonstrate the feasibility and effectiveness of these quantum classifiers, showing comparable or superior performance to classical domain adaptation models. The article highlights open questions and future work in optimizing these quantum algorithms for practical implementation.