Targeted Pseudorandom Generators, Simulation Advice Generators, and Derandomizing Logspace

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 $$ \bigcap_{\alpha > 0} \mathbf{promise\text{-}BPSPACE}(\log^{1 + \alpha} n) = \bigcap_{\alpha > 0} \mathbf{promise\text{-}DSPACE}(\log^{1 + \alpha} n)$$ 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.

There's a well-known conjecture in complexity theory ("$\mathbf{L} = \mathbf{BPL}$") that says that randomness isn't necessary for solving problems using a small amount of computer memory. The most promising approach to proving this conjecture is to design a suitable kind of "pseudorandom generator". But in principle, one could hope to prove $\mathbf{L} = \mathbf{BPL}$ using a totally different approach. In this paper, we prove a theorem that can be roughly stated as follows. If every true statement along the lines of $\mathbf{L} = \mathbf{BPL}$ can be proven using the pseudorandom generator approach, then in fact $\mathbf{L} = \mathbf{BPL}$ (actually the conclusion is slightly weaker).