Exactly simulating stochastic chemical reaction networks in sub-constant time per reaction

The model of chemical reaction networks is among the oldest and most widely studied and used in natural science. The model describes reactions among abstract chemical species, for instance $A + B \to C$, which indicates that if a molecule of type $A$ interacts with a molecule of type $B$ (the reactants), they may stick together to form a molecule of type $C$ (the product). The standard algorithm for simulating (discrete, stochastic) chemical reaction networks is the Gillespie algorithm [JPC 1977], which stochastically simulates one reaction at a time, so to simulate $\ell$ consecutive reactions, it requires total running time $Ω(\ell)$. We give the first chemical reaction network stochastic simulation algorithm that can simulate $\ell$ reactions, provably preserving the exact stochastic dynamics (sampling from precisely the same distribution as the Gillespie algorithm), yet using time provably sublinear in $\ell$. Under reasonable assumptions, our algorithm can simulate $\ell$ reactions among $n$ total molecules in time $O(\ell/\sqrt n)$ when $\ell \ge n^{5/4}$, and in time $O(\ell/n^{2/5})$ when $n \le \ell \le n^{5/4}$. Our work adapts an algorithm of Berenbrink, Hammer, Kaaser, Meyer, Penschuck, and Tran [ESA 2020] for simulating the distributed computing model known as population protocols, extending it (in a very nontrivial way) to the more general chemical reaction network setting. We provide an implementation of our algorithm as a Python package, with the core logic implemented in Rust, with remarkably fast performance in practice.