  We describe tensor network algorithms to optimize quantum circuits for
adiabatic quantum computing. To suppress diabatic transitions, we include
counterdiabatic driving in the optimization and utilize variational matrix
product operators to represent adiabatic gauge potentials. Traditionally,
Trotter product formulas are used to turn adiabatic time evolution into quantum
circuits and the addition of counterdiabatic driving increases the circuit
depth per time step. Instead, we classically optimize a parameterized quantum
circuit of fixed depth to simultaneously capture adiabatic evolution together
with counterdiabatic driving over many time steps. The methods are applied to
the ground state preparation of quantum Ising chains with transverse and
longitudinal fields. We show that the classically optimized circuits can
significantly outperform Trotter product formulas. Additionally, we discuss how
the approach can be used for combinatorial optimization.
