Variational Quantum Gibbs State Preparation with a Truncated Taylor Series

The preparation of quantum Gibbs state is an essential part computation and has wide-ranging applications in various areas, including simulation, optimization, machine learning. In this paper, we propose variational hybrid quantum-classical algorithms for preparation. We first utilize a truncated Taylor series to evaluate the free energy choose as loss function. Our protocol then trains parameterized circuits learn desired state. Notably, algorithm can be implemented on near-term computers equipped with circuits. By performing numerical experiments, show that shallow only one additional qubit trained prepare Ising chain spin states fidelity higher than 95%. particular, model, find simplified circuit ansatz parameter realize 99% at inverse temperatures larger 2.