  The preparation of Gibbs thermal states is an important task in quantum
computation with applications in quantum simulation, quantum optimization, and
quantum machine learning. However, many algorithms for preparing Gibbs states
rely on quantum subroutines which are difficult to implement on near-term
hardware. Here, we address this by (i) introducing an objective function that,
unlike the free energy, is easily measured, and (ii) using dynamically
generated, problem-tailored ans\"atze. This allows for arbitrarily accurate
Gibbs state preparation using low-depth circuits. To verify the effectiveness
of our approach, we numerically demonstrate that our algorithm can prepare
high-fidelity Gibbs states across a broad range of temperatures and for a
variety of Hamiltonians.
