Efficiency of k-Local Quantum Search and its Adiabatic Variant on Random k-SAT

The computational complexity of random $k$-SAT problem is contingent on the clause number $m$. In classical computing, a satisfiability threshold is identified at $m=r_k n$, marking the transition of random $k$-SAT from solubility to insolubility. However, beyond this established threshold, comprehending the complexity remains challenging. On quantum computers, direct application of Grover's unstructured quantum search still yields exponential time requirements due to oversight of structural information. This paper introduces a family of structured quantum search algorithms, termed $k$-local quantum search, designed to address the $k$-SAT problem. Because search algorithm necessitates the presence of a target, our focus is specifically on the satisfiable side of $k$-SAT, i.e., max-$k$-SAT on satisfiable instances, denoted as max-$k$-SSAT, with a small $k \ge 3$. For random instances with $m=\Omega(n^{{2+\epsilon})$,} general exponential acceleration is proven for any small $\epsilon>0$ and sufficiently large $n$. Furthermore, adiabatic $k$-local quantum search improves the bound of general efficiency to $m=\Omega(n^{{1+\epsilon})$,} within an evolution time of $\mathcal{O}(n^2)$. Specifically, for $m=\Theta(n^{{1+\delta+\epsilon})$,} the efficiency is guaranteed in a probability of $1-\mathcal{O}(\mathrm{erfc}(n^{{\delta/2}))$.} By modifying this algorithm capable of solving all instances, we prove that the max-$k$-SSAT is polynomial on average if $m=\Omega(n^{{2+\epsilon})$} based on the average-case complexity theory.