MPS TE-PAI achieves unbiased classical time evolution by averaging tensor-network representations of randomized shallow Trotter circuits, yielding lower gate counts per sample and better tolerance to bond-dimension truncation than standard methods.
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Tailoring tensor network algorithms to the scale hierarchy in quantics representation produces faster, more robust solvers for high-dimensional linear and eigenvalue PDE problems.
Backreaction in semiclassical scalar QED in 1+1D avoids instabilities and produces over-screening at high external charges.