  Probing correlated states of many-body systems is one of the central tasks
for quantum simulators and processors. A promising approach to state
preparation is to realize desired correlated states as steady states of
engineered dissipative evolution. A recent experiment with a Google
superconducting quantum processor [X. Mi et al., Science 383, 1332 (2024)]
demonstrated a cooling algorithm utilizing auxiliary degrees of freedom that
are periodically reset to remove quasiparticles from the system, thereby
driving it towards the ground state. We develop a kinetic theory framework to
describe quasiparticle cooling dynamics, and employ it to compare the
efficiency of different cooling algorithms. In particular, we introduce a
protocol where coupling to auxiliaries is modulated in time to minimize heating
processes, and demonstrate that it allows a high-fidelity preparation of ground
states in different quantum phases. We verify the validity of the kinetic
theory description by an extensive comparison with numerical simulations of a
1d transverse-field Ising model using a solvable model and tensor-network
techniques. Further, the effect of noise, which limits efficiency of
variational quantum algorithms in near-term quantum processors, can be
naturally described within the kinetic theory. We investigate the steady state
quasiparticle population as a function of noise strength, and establish maximum
noise values for achieving high-fidelity ground states. This work establishes
quasiparticle cooling algorithms as a practical, robust method for many-body
state preparation on near-term quantum processors.
