  In this paper, we introduce the quantum adaptive distribution search (QuADS),
a quantum continuous optimization algorithm that integrates Grover adaptive
search (GAS) with the covariance matrix adaptation - evolution strategy
(CMA-ES), a classical technique for continuous optimization. QuADS utilizes the
quantum-based search capabilities of GAS and enhances them with the principles
of CMA-ES for more efficient optimization. It employs a multivariate normal
distribution for the initial state of the quantum search and repeatedly updates
it throughout the optimization process. Our numerical experiments show that
QuADS outperforms both GAS and CMA-ES. This is achieved through adaptive
refinement of the initial state distribution rather than consistently using a
uniform state, resulting in fewer oracle calls. This study presents an
important step toward exploiting the potential of quantum computing for
continuous optimization.
