Targeted Pseudorandom Generators, Simulation Advice Generators, and Derandomizing Logspace

TL;DR

This paper explores the relationship between pseudorandom generators, simulation advice generators, and derandomization in logspace, establishing equivalences and transformations that deepen understanding of computational complexity and derandomization techniques.

Contribution

It proves an equivalence between targeted pseudorandom generators and simulation advice generators for logspace, advancing the theoretical framework of derandomization in complexity theory.

Findings

Equivalence between targeted pseudorandom generators and simulation advice generators in logspace.

Strengthened the connection between derandomization results and pseudorandom generator strength.

Demonstrated transformations of targeted pseudorandom generators into simulation advice generators in uniform settings.

Abstract

Assume that for every derandomization result for logspace algorithms, there is a pseudorandom generator strong enough to nearly recover the derandomization by iterating over all seeds and taking a majority vote. We prove under a precise version of this assumption that $\mathbf{BPL} \subseteq \bigcap_{\alpha > 0} \mathbf{DSPACE}(\log^{1 + \alpha} n)$. We strengthen the theorem to an equivalence by considering two generalizations of the concept of a pseudorandom generator against logspace. A targeted pseudorandom generator against logspace takes as input a short uniform random seed and a finite automaton; it outputs a long bitstring that looks random to that particular automaton. A simulation advice generator for logspace stretches a small uniform random seed into a long advice string; the requirement is that there is some logspace algorithm that, given a finite automaton and this advice string, simulates the automaton reading a long uniform random input. We prove that $\bigcap_{\alpha > 0} \mathbf{promise\mbox{-}BPSPACE}(\log^{1 + \alpha} n) = \bigcap_{\alpha > 0} \mathbf{promise\mbox{-}DSPACE}(\log^{1 + \alpha} n)$ if and only if for every targeted pseudorandom generator against logspace, there is a simulation advice generator for logspace with similar parameters. Finally, we observe that in a certain uniform setting (namely, if we only worry about sequences of automata that can be generated in logspace), targeted pseudorandom generators against logspace can be transformed into simulation advice generators with similar parameters.