Operator backflow and the classical simulation of quantum transport

TL;DR

This paper develops a theory for backflow corrections in quantum transport simulations, showing they are exponentially suppressed with cutoff size and enabling more memory-efficient classical simulations of many-body quantum dynamics.

Contribution

It introduces a systematic theory for backflow corrections affecting transport coefficients, improving understanding of memory scaling in classical simulations of quantum dynamics.

Findings

Backflow corrections are exponentially suppressed in cutoff size.

Transport coefficients can be approximated with memory scaling as exp(O(log(ε)^2)).

Numerical tests confirm the theoretical predictions.

Abstract

Tensor product states have proved extremely powerful for simulating the area-law entangled states of many-body systems, such as the ground states of gapped Hamiltonians in one dimension. The applicability of such methods to the \emph{dynamics} of many-body systems is less clear: the memory required grows exponentially in time in most cases, quickly becoming unmanageable. New methods reduce the memory required by selectively discarding/dissipating parts of the many-body wavefunction which are expected to have little effect on the hydrodynamic observables typically of interest: for example, some methods discard fine-grained correlations associated with $n$-point functions, with $n$ exceeding some cutoff $\ell_*$. In this work, we present a theory for the sizes of `backflow corrections', i.e., systematic errors due to discarding this fine-grained information. In particular, we focus on their effect on transport coefficients. Our results suggest that backflow corrections are exponentially suppressed in the size of the cutoff $\ell_*$. Moreover, the backflow errors themselves have a hydrodynamical expansion, which we elucidate. We test our predictions against numerical simulations run on random unitary circuits and ergodic spin-chains. These results lead to the conjecture that transport coefficients in ergodic diffusive systems can be captured to a given precision $\epsilon$ with an amount of memory scaling as $\exp[\mathcal{O}(\log(\epsilon)^2)]$, significantly better than the naive estimate of memory $\exp[\mathcal{O}(\mathrm{poly}(\epsilon^{-1}))]$ required by more brute-force methods.