  Solving optimization problems using variational algorithms stands out as a
crucial application for noisy intermediate-scale devices. Instead of
constructing gate-based quantum computers, our focus centers on designing
variational quantum algorithms within the analog paradigm. This involves
optimizing parameters that directly control pulses, driving quantum states
towards target states without the necessity of compiling a quantum circuit. In
this work, we introduce pulse-based variational quantum optimization (PBVQO) as
a hardware-level framework. We illustrate the framework by optimizing external
fluxes on superconducting quantum interference devices, effectively driving the
wave function of this specific quantum architecture to the ground state of an
encoded problem Hamiltonian. Given that the performance of variational
algorithms heavily relies on appropriate initial parameters, we introduce a
global optimizer as a meta-learning technique to tackle a simple problem. The
synergy between PBVQO and meta-learning provides an advantage over conventional
gate-based variational algorithms.
