To　achieve　multi-class　image　reranking,　a　novel　image　reranking　algorithm　using　multiple　discrete-time　quantum　walk　is　proposed.　In　this　algorithm,　a　weighted　undirected　complete　graph　is　first　constructed,　in　which　the　nodes　for　the　graph　represent　the　images　and　the　weighted　values　of　these　edges　are　the　similarity　value　between　the　images.　Secondly,　it　uses　the　spectral　clustering　to　divide　the　images　into　k　classes　and　finds　the　representative　image　of　each　class.　Thirdly,　it　uses　the　k　representative　images　as　the　initial　state　of　quantum　system,　and　the　flip-flop　shift　operator　and　the　weighted　coin　operator　are　used　to　control　multiple　discrete-time　quantum　walk　on　the　weighted　complete　graph.　Finally,　the　average　probability　values　of　the　walker　reaching　the　node　of　the　graph　is　used　as　the　relevance　scores　of　the　image,　and　then　the　images　are　reranked　by　the　relevance　scores.　the　experimental　results　show　that　our　scheme　has　a　significant　enhance　compared　with　the　initial　ranking　algorithm　from　the　comparison　of　visual　and　relevance　scores.　Furthermore,　the　effectiveness　of　our　algorithm　is　evaluated　by　the　average　precision　(AP)　and　the　mean　average　precision　(MAP),　where　the　AP　of　our　algorithm　is　increased　by　53.21%,　31.75%　and　14.29%　for　three　types　of　the　query　image　in　randomly　selected　image　group　respectively,　and　the　MAP　of　our　algorithm　is　increased　by　29.57%　for　all　image　groups　compared　with　the　initial　ranking　algorithm.　　©　2022　IEEE.