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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New protocols achieve exact log-depth preparation of translationally invariant and random MPS with improved scalings Õ(χ² log L + χ⁴) using block-encoded corrections and generalized amplitude amplification.
Continuous TE-PAI provides an unbiased randomized protocol for Hamiltonian simulation free of Trotter error at finite circuit depth, combined with structure-aware variance reduction that achieves up to 96% sampling-cost savings in n=30 tensor-network simulations.
citing papers explorer
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Quantum-inspired classical simulation through randomized time evolution
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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Exact log-depth preparation of highly entangled matrix product states
New protocols achieve exact log-depth preparation of translationally invariant and random MPS with improved scalings Õ(χ² log L + χ⁴) using block-encoded corrections and generalized amplitude amplification.
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Structure-Aware Variance Reduction for Unbiased Randomized Hamiltonian Simulation
Continuous TE-PAI provides an unbiased randomized protocol for Hamiltonian simulation free of Trotter error at finite circuit depth, combined with structure-aware variance reduction that achieves up to 96% sampling-cost savings in n=30 tensor-network simulations.