Team#apmcmlz2400090
TeamNumber:apmcmlz2400090
ProblemChosen:A
ApplicationresearchoftheQUBOmodelinfeatureselectionand
imageclassification
Summary
Withtherapiddevelopmentofquantumcomputingtechnology,itspotentialindealingwith
complexproblemsandlarge-scaledatasetshasbecomeincreasinglyprominent,especiallyinthe
fieldofartificialintelligence(AI).Thisarticlefocusesonthedeepintegrationofquantumcomputing
andAIandexploresitspracticalapplicationintwokeytasks:featureselectionandimageclassifi-
cation.ByconvertingtheproblemintoaQUBOmodelandcombiningitwiththesimulatedanneal-
ingalgorithmprovidedbyKaiwuSDK,theperformanceoftheAImodelisoptimized,andthebot-
tleneckproblemoftraditionalcomputingmethodsinhigh-dimensionaldataprocessinganddeep
learningmodeloptimizationissolved,providinginnovativeideasforimprovingtaskefficiencyand
classificationaccuracy.
Datapreprocessing,fortheGermancreditscoredatasetinquestion1,thispapersolvesthe
problemofmissingvaluesandoutliersinthedatathroughmethodssuchasdescriptivestatistics,
binning,andinterpolationfilling;nonlinearBox-Coxtransformationandfeaturenormalizationtech-
nologyareusedtooptimizethedatastructuretoensuretheeffectivenessoffeatureselection.For
question2,thispaperselectsFASHION-MNISTandCIFARdatasetsandprovideshigh-qualitydata
inputforsubsequentimageclassificationmodelsthroughpreprocessingmethodssuchasimageflip-
ping,rotation,denoising,andnormalization.
Fortask1,featureselectionoftheGermancreditscoredataset.FortheGermancreditscore
dataset,thispaperconductedfeatureselectionresearchbasedontheLASSOmodeland