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1、UniversaldiscriminativequantumneuralnetworksH.Chen1,2,L.Wossnig2,*,S.Severini2,3,H.Neven4,andM.Mohseni4,y1DepartmentofPhysics&Astronomy,UniversityCollegeLondon,London,UK2DepartmentofComputerScience,UniversityCollegeLondon,London,UK3InstituteofNaturalSciences,ShanghaiJiaoTongUniversity,
2、Shanghai,China4GoogleQuantumAILaboratory,Venice,California,USAAbstractQuantummechanicsfundamentallyforbidsdeterministicdiscriminationofquan-tumstatesandprocesses.However,theabilitytooptimallydistinguishvariousclassesofquantumdataisanimportantprimitiveinquantuminformationscience.Inthisw
3、ork,wetrainnear-termquantumcircuitstoclassifydatarepresentedbynon-orthogonalquantumprobabilitydistributionsusingtheAdamstochasticopti-mizationalgorithm.Thisisachievedbyiterativeinteractionsofaclassicaldevicewithaquantumprocessortodiscovertheparametersofanunknownnon-unitaryquantumcircui
4、t.Thiscircuitlearnstosimulatestheunknownstructureofagener-alizedquantummeasurement,orPositive-Operator-Value-Measure(POVM),thatisrequiredtooptimallydistinguishpossibledistributionsofquantuminputs.No-tablyweuseuniversalcircuittopologies,withatheoreticallymotivatedcircuitde-sign,whichgua
5、ranteesthatourcircuitscaninprinciplelearntoperformarbitraryinput-outputmappings.Ournumericalsimulationsshowthatshallowquantumcircuitscouldbetrainedtodiscriminateamongvariouspureandmixedquantumstatesexhibitingatrade-offbetweenminimizingerroneousandinconclusiveout-comeswithcomparableperf
6、ormancetotheoreticallyoptimalPOVMs.Wetrainthecircuitondifferentclassesofquantumdataandevaluatethegeneralizationerroronunseenmixedquantumstates.Thisgeneralizationpowerhencedistin-guishesourworkfromstandardcircuitoptimizationandprovidesanexampleofquantummachinelearningforataskthathasinhe
7、rentlynoclassicalanalogue.1IntroductionTheinterfaceofquantumphysicsandmachinelearninghasrecentlyreceivedaconsiderableamountofinterest.TwocomplementarymethodologieshavebeendevelopedtoaddressthequestionwhetherarXiv:1805.08654v1[quant-ph]22May2018quantummechanicscanhelpwithsolvingmachin
