In　order　to　further　improve　the　performance　of　image　retrieval,　a　novel　image　reranking　algorithm　based　on　discrete-time　quantum　walk　is　proposed.　In　this　algorithm,　a　discrete-time　quantum　walk　model　based　on　a　weighted　undirected　complete　graph　is　first　constructed,　in　which　the　nodes　of　the　graph　represent　the　images　and　the　weighted　values　of　these　edges　are　the　similarity　value　between　the　images.　Then　the　average　probability　values　of　the　walker　reaching　the　node　of　the　graph　is　used　as　the　relevance　scores　of　the　image　and　the　images　are　reranked　according　to　the　relevance　scores.　Finally,　our　experimental　results　show　that　our　proposed　reranking　algorithm　has　a　significant　improvement　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　18.23%　and　37.61%　for　two　types　of　the　query　image　in　randomly　selected　image　group　respectively,　and　the　MAP　of　our　algorithm　is　increased　by　22.24%　for　all　image　groups　compared　with　the　initial　ranking　algorithm.