QSETH strikes again: finer quantum lower bounds for lattice problem, strong simulation, hitting set problem, and more

TL;DR

This paper uses the QSETH framework to establish quantum lower bounds for various problems, providing insights into their inherent complexity and implications for quantum computing and cryptography.

Contribution

It extends the QSETH framework to analyze the quantum complexity of variants of CNFSAT and related problems, offering new lower bounds and detailed methodology guidance.

Findings

Quantum lower bounds for parity-CNFSAT and counting-CNFSAT.

Implications for lattice problems, strong simulation, and hitting set problems.

Enhanced understanding of the QSETH framework's application.

Abstract

While seemingly undesirable, it is not a surprising fact that there are certain problems for which quantum computers offer no computational advantage over their respective classical counterparts. Moreover, there are problems for which there is no `useful' computational advantage possible with the current quantum hardware. This situation however can be beneficial if we don't want quantum computers to solve certain problems fast - say problems relevant to post-quantum cryptography. In such a situation, we would like to have evidence that it is difficult to solve those problems on quantum computers; but what is their exact complexity? To do so one has to prove lower bounds, but proving unconditional time lower bounds has never been easy. As a result, resorting to conditional lower bounds has been quite popular in the classical community and is gaining momentum in the quantum community. In this paper, by the use of the QSETH framework [Buhrman-Patro-Speelman 2021], we are able to understand the quantum complexity of a few natural variants of CNFSAT, such as parity-CNFSAT or counting-CNFSAT, and also are able to comment on the non-trivial complexity of approximate-#CNFSAT; both of these have interesting implications about the complexity of (variations of) lattice problems, strong simulation and hitting set problem, and more. In the process, we explore the QSETH framework in greater detail than was (required and) discussed in the original paper, thus also serving as a useful guide on how to effectively use the QSETH framework.