We show that approximations of $\#\mathsf{SAC}^1$ functions (i.e. functions computed by logspace-uniform polynomial-size positive semi-unbounded arithmetic circuits of depth $O(\log n)$) can be computed in logspace uniform $\mathsf{AC}^1$ (denoted as $\mathsf{L}\text{-}\mathsf{AC}^1$) with multiplicative error $2^{-\operatorname{poly}\log n}$.Previously, approximations of $\#\mathsf{SAC}^1$ functions were known to be in logspace-uniform $\mathsf{TC}^1$ via exact computation given by Allender, Jiao, Mahajan, and Vinay (TCS'98). A main corollary of our result is that the following models beyond $\mathsf{BPL}$ are computable by $\mathsf{L}\text{-}\mathsf{AC}^1$ with multiplicative error $2^{-\operatorname{poly}\log n}$: - Probabilistic Auxiliary Push-down Machines (Probabilistic $\mathsf{AuxPDM}$s), which are polynomial time logspace Turing Machines (TMs) with an auxiliary stack having unbounded size and a read-once random tape; - Read-$k$ Probabilistic TMs with polynomial time and logarithmic space, where $k$ is a constant and Read-$k$ means the random tape is two-way accessible and each cell can be visited for at most $k$ times. Notice that this is a direct generalization and improvement for $\mathsf{BPL}\subseteq\mathsf{L}\text{-}\mathsf{AC}^1$ of Cheng and Wang (CCC'24), and the approximation error is improved from additive reverse polynomial to multiplicative reverse quasi-polynomial. $\mathsf{AuxPDM}$ was introduced by Cook (JACM'71) and its probabilistic version was first studied by Macarie and Ogihara (TCS'98). Read-$k$ Probabilistic logspace TMs was studied by Impagliazzo, Nisan, Wigderson (STOC'94) where a pseudorandom generator for this model was given. We mention that multiplicative error approximations are stronger than additive error approximations in the sense that they can even be used to decide non-deterministic computations.

翻译：暂无翻译