  Matrix Product States (MPS) and Operators (MPO) have been proven to be a
powerful tool to study quantum many-body systems but are restricted to
moderately entangled states as the number of parameters scales exponentially
with the entanglement entropy. While MPS can efficiently find ground states of
1D systems, their capacities are limited when simulating their dynamics, where
the entanglement can increase ballistically with time. On the other hand,
quantum devices appear as a natural platform to encode and perform the time
evolution of correlated many-body states. However, accessing the regime of
long-time dynamics is hampered by quantum noise. In this study we use the best
of worlds: the short-time dynamics is efficiently performed by MPSs, compiled
into short-depth quantum circuits, and is performed further in time on a
quantum computer thanks to efficient MPO-optimized quantum circuits. We
quantify the capacities of this hybrid classical-quantum scheme in terms of
fidelities taking into account a noise model. We show that using classical
knowledge in the form of tensor networks provides a way to better use limited
quantum resources and lowers drastically the noise requirements to reach a
practical quantum advantage. Finally we successfully demonstrate our approach
with an experimental realization of the technique. Combined with efficient
circuit transpilation we simulate a 10-qubit system on an actual quantum device
over a longer time scale than low-bond-dimension MPSs and purely quantum
Trotter evolution.
