Optimization　drives　advances　in　quantum　science　and　machine　learning,　yet　most　generative　models　aim　to　mimic　data　rather　than　to　discover　optimal　answers　to　challenging　problems.　Here　we　present　a　variational　generative　optimization　network　that　learns　to　map　simple　random　inputs　into　high　quality　solutions　across　a　variety　of　quantum　tasks.　We　demonstrate　that　the　network　rapidly　identifies　entangled　states　exhibiting　an　optimal　advantage　in　entanglement　detection　when　allowing　classical　communication,　attains　the　ground　state　energy　of　an　eighteen　spin　model　without　encountering　the　barren　plateau　phenomenon　that　hampers　standard　hybrid　algorithms,　and—after　a　single　training　run—outputs　multiple　orthogonal　ground　states　of　degenerate　quantum　models.　Because　the　method　is　model　agnostic,　parallelizable　and　runs　on　current　classical　hardware,　it　can　accelerate　future　variational　optimization　problems　in　quantum　information,　quantum　computing　and　beyond.　©　The　Author(s)　2025.