Hydrogeological　parameters　are　important　indicators　for　studying　aquifer　properties　and　constructing　numerical　models.　Affected　by　various　unknown　underground　factors　or　human　factors,　there’s　still　a　big　gap　between　the　hydrogeological　parameters　of　aquifers　calculated　by　different　traditional　pumping　test　methods.　In　recent　years,　artificial　intelligence　algorithms　have　been　successfully　applied　to　aquifer　parameter　inversion,　but　for　a　single　intelligence　algorithm,　each　has　its　respective　shortcomings.　This　paper　combined　the　quantum　computing　theory　with　the　Artificial　Fish　Swarm　Algorithm　(AFSA)　to　improve　the　performance　of　AFSA,　and　then　proved　the　correctness　and　superiority　of　the　proposed　method　via　examples.　©　2019　Lavoisier.　All　rights　reserved.