In this paper, we propose a novel and powerful method to harness Bayesian optimization for V ariational Quantum Eigensolvers (VQEs)--a hybrid quantum-classical protocol used to approximate the ground state of a quantum Hamiltonian.

Vision-language grounding is a fundamental and crucial problem in multi-modal understanding.

Wefirst establish minimax lower bounds for the estimation error, given avector of privacyguarantees, and show that a linear estimator is (near) optimal.