  This research employs the Kraus representation and Sz.-Nagy dilation theorem
to model a three-level quantum heat on quantum circuits, investigating its
dynamic evolution and thermodynamic performance. The feasibility of the dynamic
model is validated by tracking the changes of population. On the basis of
reinforcement learning algorithm, the optimal cycle of the quantum heat engine
for maximal average power is proposed and verified by the thermodynamic model.
The stability of quantum circuit simulations is scrutinized through a
comparative analysis of theoretical and simulated results, predicated on an
orthogonal test. These results affirm the practicality of simulating quantum
heat engines on quantum circuits, offering potential for substantially
curtailing the experimental expenses associated with the construction of such
engines.
