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

Adaptive variational algorithms for quantum Gibbs state preparation