Quantum

ComputingforTransportationandLogistics

March2024

1|Transportation&Logistics

Acknowledgments

ThankyoutotheQuantumEconomicDevelopmentConsortium(QED-C?)UseCases

TechnicalAdvisoryCommitteeandtheNorthwesternUniversityTransportationCenter.Thisreportwouldnothavebeenpossiblewithouttheleadershipandcontributionsofthe

organizingcommitteeinparticular:

●StephenBrobst,Teradata

●CarlDukatz,Accenture

●KevinGlynn,NorthwesternUniversity

●BretJohnson,NorthwesternUniversityTransportationCenter

●ClaireLecornu,SRI/QED-C

●AlexLuna,AlphaRail

●AllisonSchwartz,D-Wave

TheNationalInstituteofStandardsandTechnologyprovided?nancialsupportforthisstudy.

AboutQED-C

QED-Cisanindustry-drivenconsortiummanagedbySRIInternational.Withadiverse

membershiprepresentingindustry,academia,government,andotherstakeholders,theconsortiumseekstoenableandgrowthequantumindustryandassociatedsupplychain.FormoreaboutQED-C,visitourwebsiteat

.

Suggestedcitation

QuantumEconomicDevelopmentConsortium(QED-C).QuantumComputingforTransportationandLogistics.Arlington,VA:March2024.

/xport24.

Governmentpurposerights

AgreementNo.:OTA-2019-0001

ContractorName:SRIInternational

ContractorAddress:333RavenswoodAvenue,MenloPark,CA94025

ExpirationDate:Perpetual

Use,duplication,ordisclosureissubjecttotherestrictionsasstatedintheAgreementbetweenNISTandSRI.

Non-U.S.Governmentnotice

Copyright?2024SRI.Allrightsreserved.

Disclaimer

ThispublicationoftheQuantumEconomicDevelopmentConsortium,whichismanagedbySRIInternational,doesnotnecessarilyrepresenttheviewsofSRIInternational,anyindividualmemberofQED-C,oranygovernmentagency.

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TableofContents

ExecutiveSummary 4

Introduction 8

DecisionMakinginSupplyChainswithQuantumComputing 12

UseCasesImpactandFeasibility 13

Classi?cation 15

ImpactandFeasibilitybyRelevantIndustry 17

ImpactandFeasibilitybyApproach 18

ImpactandFeasibilitybyAreaofOperation 19

PriorityUseCases 23

Recommendations 27

Conclusions 29

AppendixA:Methodology 31

WorkshopGoals:Surfacehigh-impact,feasibleideas 31

Structure:Encouragecollaboration,freshthinking 31

Valuechainmatrix:Abidirectional?ow 33

Workshopprocess:Ideagenerator 33

Ideas:Brainstorm,analysis,selection 33

Conceptcards:Winnowingideas 34

A?exibleratingsystemtopromoteexpansivethinking 34

Conceptposters:Howtoexecute 34

AppendixB:QuantumComputingUseCasesforTransportationandLogistics 36

AppendixC:WorkshopParticipants 42

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ExecutiveSummary

Toassessthecurrentstateandfuturepossibilitiesofquantumcomputingandthe

transportationandlogisticsindustry,QED-C’sUseCasesTechnicalAdvisoryCommitteeledthisstudybasedonaworkshopexploringthefeasibilityandimpactofdi?erentusecases.

1

Quantumcomputing(QC)o?ersintriguingsolutionstosupplychain,transportation,and

logisticsproblemsthatclassicalcomputerscannotcompletelysolve.Italsoo?ersthepossibilityofsigni?cantlyfastercomputations,withapplicationsinallmodesofthe

transportationandlogisticsindustry—air,land,andsea.

Thechallengesfacedbythetransportationandlogisticsindustryincludeoptimizationof

inventoryacrossmanyfacilities,routeplanning,minimizationofmanufacturingcosts,last-

miledelivery,factoryandtruckscheduling,dynamicpricingalgorithms,?eetmanagementandmaintenance,sustainabilityandgreenlogistics,energysystems,controlofautonomousvehicles,andnavigationwithinmoderncities.Theliteraturesuggeststhatquantum

computingo?ersadvantagesinthreeprimaryareas:optimization,machinelearning,andsimulation.

Aspartofthisstudy,expertsfromboththetransportationandlogisticsindustryandthe

quantumcommunitiesconvenedataworkshoptoidentifyusecasesattheintersectionofthetwo?elds.Theoverwhelmingmajorityofusecasesidenti?edwereultimately

optimizationproblems,mostofwhichcamedowntoplanningoperations.Incontrast,use

casesapplyingasimulation(fromalogisticsindustryperspective)approachwereseenbyexpertsaslessfeasibleandimpactfulthanmostoptimizationproblems.Severalcompanieshavedemonstratedsmall-scaleprototypesthatapplyQCalgorithmstodi?erentclassesoflogisticsproblems.

2

Fromanalysisofthecurrentstateandthefeasibilityandimpactofusecasesofquantum

computingfortransportationandlogisticsapplications,fourusecasesemergeasthosethatcouldhavethegreatestimpactintherelativelynearterm:

?Optimizationoflaborplans

?Continuousrouteoptimization

?Optimizationofwarehousing

?Demandforecasting

Inaddition,thisreportputsforward?verecommendationsforboostingdevelopmentandadoptionofQCtechnologiesbythetransportationandlogisticsindustry:

1Thestudymethodology,identifiedusecases,andworkshopparticipantsarepresentedinAppendicesA,B,andC.

2See,e.g.,SeanJ.Weinberg,FabioSanches,TakanoriIde,KazumitzuKamiya,andRandallCorrell2023.Supply

chainlogisticswithquantumandclassicalannealingalgorithms.ScientificReports13:4770,doi:

10.1038/s41598-

023-31765-8;

ChristopherD.B.Bentley,SamuelMarsh,AndréR.R.Carvalho,PhilipKilby,andMichaelJ.Biercuk.

2022.Quantumcomputingfortransportoptimization.arXiv,arXiv:2206.07313;andCrispinH.V.Cooper.2022.ExploringPotentialApplicationsofQuantumComputinginTransportationModelling,IEEETransactionsonIntelligentTransportationSystems23,no.9:14712–20,doi:

10.1109/TITS.2021.3132161.

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?Increaseoperationale?ciencyforbusinesses:Quantumcomputingo?erspossiblesolutionsforincreasedbusinesse?cienciesviabetterroutingprograms,e?cient

cargoloading,optimizedmanufacturingprocesses,andoptimallaborscheduling.

However,QCadoptioncanbeprohibitivelyexpensiveandrisky,especiallyforsmallercompanies.Toovercomethisbarrier,QCcompaniescouldo?erdiscountedratesforsmalllogisticscompaniestotrialthetechnologyandseethebusinesse?ciencies

thatcanbegained.ThiswouldalsoprovidevaluablefeedbackanddatatotheQCcompanytoimprovetheirproduct.Thiscross-industrycollaborationcouldeven

facilitatethedevelopmentofquantum-enabledrouteplanningandotheroptimizationtools.

Relatedly,ManufacturingUSAisanetworkofinstitutesthateachhaveadistinct

technologyfocusbutwithacommongoal:tosecurethefutureofUSmanufacturingthroughinnovation,education,andcollaboration.Emergingtechnologieslike

quantumcomputerscouldimpactallofthetechnologies,rangingfrom?exible

electronicstobiomaterials.TheManufacturingUSAprogramshoulddisseminate

informationaboutquantumcomputingandotheremergingtechnologiesacrossthenetworktoensurebroadincorporationasadvancedmanufacturingprocessesare

beingdeveloped.

?Increasesupplychainsecurityandresilience:Therearebroaddependencies

betweenthesecurityandresilienceofnationalandglobalsupplychains.Two

measurescansupportcontinuedinnovationinthisarea:(1)identi?cationofthe

weakestlinksinsupplychainsanddirectionofenhancementstothoseareas,and(2)increasedcapabilitiesforcontingencyplanningtools.QCtechnologiescouldanalyzemoredataacrossmorevariablesandconstraintsthancanclassicalcomputers,

enablingthedevelopmentofmoreaccurateandcomprehensiveforecastsand

operatingplansthatbetterprotectagainstsupplychainthreats.Governmentcanboostthesecapabilitiesofquantumcomputersbycreatingtestbedsandsandboxprogramsfocusedondemonstrations,proofsofconcept,andpilotsofnear-termapplicationsforsupplychainmanagement.

?Addresssustainability:Climatechangeisatopconcernofcompaniesandnations,

andtransportation-basedemissionsarealeadingcontributor.Anypotentialfor

increasesine?cienciesthatreduceemissionsshouldbeexploredanddeveloped.

Oneofthemostimpactfulusesofquantumcomputersintransportationandlogisticsiscontinuousrouteoptimization,whichcandecreaseemissionsandfuelusage

acrosstransportmethods.Ascompaniesincreasinglylooktocuttheircarbon

footprint,theyshouldconsidertheimpactthatQCtechnologyadoptioncanhavebyhelpingthembetteroptimizeroutesandprocessesformaximumfuele?ciency.Asanaddedbonusforcompanies,usingquantumcomputingtooptimizethiswaywilllikelyleadtocostsavingsaswell.

?Optimizegovernmentlogisticsmissions:GovernmentcanbeanearlyadopterofQCsolutionsinoptimizingitsown?eetsandmissions.Forexample,theUSPostal

ServicecoulduseQCtechnologytobetterplan?eetmaintenance,schedulesta?,anddesignmorefuelandtimee?cientroutes.ByadoptingQCtechnologiesinits

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earlierstages,governmentcanguaranteerevenuestohelpsustainprivateQCcompanies.

?Createaskilledworkforce:QCtechnologyisevolvingquickly,creatingdemandforskilledworkerswhoareabletocontributetothe?eld.Thisincludesopportunitiesfortheendusersofthetechnology,suchasoperatingplandevelopersandroute

designers,toshapeitsdevelopmentandkeyfeaturesandfunctions.However,mostsupplychainworkerstodayarenotwellversedinquantumtechnology.IncludingQCeducationinindustrialandsupplychainengineeringdegreeprogramscould

increaseunderstandingandadoptionofthisnewtechnology.Trainingcouldalsobeextendedtotheexistingtransportationandlogisticsworkforcebycollaboratingwithprofessionalorganizationstoprovideknowledge,skills,andaccesstothelatestQCtools.Thistrainingcouldbeespeciallyusefulfortheworkerswhofocusonroute

planning,operatingplandesign,andforecasting,i.e.,thetasksthatcouldmostbene?tfromquantumcomputers.

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Introduction

Quantumcomputingo?ersintriguingsolutionstosupplychainandlogisticschallengesthatclassicalcomputerscannotcompletelysolve.Italsoo?ersthepossibilityofsigni?cantly

fastercomputations.Bothadvantagescanspurtheimaginationtonewsolutions,new

quantumusecases,andnewbusinessmodelsforallpartsofthetransportationandlogisticsindustry—air,land,andsea.Challengestobeaddressedincludeoptimizationofinventory

acrossmanyfacilities,routeplanning,minimizationofmanufacturingcosts,last-mile

delivery,factoryandtruckscheduling,dynamicpricingalgorithms,?eetmanagementandmaintenance,sustainabilityandgreenlogistics,energysystems,controlofautonomous

vehicles,andnavigationwithinmoderncities(e.g.,tra?c?ow,parking).McKinseyestimatesthatquantumcomputing(QC)couldhaveaneconomicimpactofasmuchas$63billionby2035,

3

andthesupplychainandlogisticsindustryispositionedtobeoneoftheearlier

benefactorsofquantumtechnology.

TheUStransportationindustryfacesnumerouschallengesthatmustbesolvedfortheUSeconomytocontinuetogrowandthriveoverthenextdecadeandbeyond.Tra?c

congestiononthenation’scapacity-constrainedhighwayinfrastructure;railservicequality;workforceplanningandwork-lifebalance;inventoryplanningandproductionmanagement;delaypropagationmitigationacrossinterconnected,multicarriersupplychains;and

greenhousegasemissionreductionareallchallengesthatcannotrealisticallybesolved

solelybyincreasedcapitalexpenditures,newvehiclepropulsiontechnologies,ornew

regulations.Solutionoptionstotheseproblemsarelimitedandinsomecasesextremely

expensive,whichcanpreventbusinessadoption.Theunmatchedutilitythatthepotentialofquantumcomputingo?erspresentsanexceptionalopportunityforUSleadership.Quantumcomputing’srelevance,promise,andeconomicviabilityinthissolutionspacecannotbe

ignored.

Theliteraturesuggeststhatquantumcomputingo?ersadvantagesinthreeprimaryareasforlogisticsandtransportation:optimization,machinelearning(ML),andsimulation(see

sidebar).Manycompaniesarealreadyexploringthesepotentialapplications.Forexample,Quantum-Southhastestedtheuseofquantumalgorithmstooptimizeaircargo,

4

andIBMandExxonMobilhavecollaboratedtoexploretheuseofquantumcomputerstooptimizeshippingroutesamidstavastnumberofmaritimecomplexities,suchasschedulingand

minimizingdistancetraveled.

5

3Gao,Scarlett,TimoM?ller,NikoMohr,AlexiaPastré,andFelixZiegler.2023.Gearingupformobility’sfuturewithquantumcomputing.McKinsey&Company,September13.

/industries/automotive-and-assembly/our-insights/gearing-up-for-mobilitys-

future-with-quantum-computing.

4Dargan,James.2022.Quantum-SouthExploresQuantumAlgorithmsforAirCargoOptimization.TheQuantum

Insider,December2.

/2022/12/02/quantum-south-explores-quantum-

algorithms-for-air-cargo-optimization/.

5Fretty,Peter.2021.CouldQuantumComputingSolveMaritimeComplexities?IndustryWeek,March31.

/technology-and-iiot/article/21159784/could-quantum-computing-solve-

maritime-complexities.

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De?ningtheterm“simulation”

Supplychainandlogisticsexpertsusetheterm"simulation”to

meanMonteCarlo-stylediscreteeventsimulationmostlyfor

modelingdemandforlogisticsservices.Thisdi?ersfromthe

quantumindustryde?nition,whichgenerallyreferstosimulationofquantummechanicalsystems(mostcommonlychemistryand

materialscience).Further,whilethesimulationdescribedby

logisticsexpertsisusuallysolvedusingmachinelearning

algorithmsinaquantumcomputer,thelogisticsexpertsinvolvedinthisstudyusedtheterm“machinelearning”tolabelproblemsof

patternrecognitionindata—whichisaclassicalcomputing

de?nition.Inthispaper,thede?nitionsof“simulation”and“machinelearning”followthelogisticsexperts’de?nition.a

aForahistoryoftheuseoftheterms“simulation”and“optimization”byindustry,

seeJean-Fran?oisCordeau,PaoloToth,andDanieleVigo,“ASurveyofOptimizationModelsforTrainRoutingandScheduling,”TransportationScience32,no.4(1998):

380–404,doi:

10.1287/trsc.32.4.380.

Manygovernmentsaresupportingthedevelopmentandapplicationofquantum

technologies.IntheUnitedStates,forexample,SandiaNationalLaboratoriesisdevelopingQCalgorithmstosolvesupplychainoptimizationproblems.

6

Furthermore,theUSCongresscalledforareviewofwhatapplicationscanbedevelopedintheneartermusingtoday’s

quantumtechnology;inresponsetothis,QED-Cdetailedhowpublic-privatepartnerships

shouldbeusedtoadvanceapplicationdevelopment.

7

Additionally,Congressis

reauthorizingtheNationalQuantumInitiativeAct

8

withnewlanguagethatfocusessupportonnear-andmid-termapplicationdevelopmentthroughtestbedprograms.

9

CongressalsopassedtheNationalDefenseAuthorizationAct(NDAA)inJanuary2023,

10

whichincludesapilotprogramtodevelopquantumapplications.Bothpiecesoflegislationencourage

collaborationwithindustry,includingconsiderationofquantumannealing,gatemodel,andquantum-hybridtechnologies,tomovethemostpromisingofquantumtechnologiesoutofthelabandintocommercialapplications.OthercountriesinvestinginQCtechnologiesto

developquantumapplicationsincludetheUnitedKingdom’scallforquantumapplication

6Law,Marcus.2023.Howquantumcomputingcansolvesupplychainchallenges.SupplyChain,February16.

/pr_newswire/how-quantum-computing-can-solve-supply-chain-challenges.

7QuantumEconomicDevelopmentConsortium(QED-C).2022.PublicPrivatePartnershipsinQuantumComputing:ThePotentialforAcceleratingNear-TermQuantumApplications.Arlington,VA.

/ppp22/

.

8USCongress,House.2023.ABilltoreauthorizetheNationalQuantumInitiativeAct,andforotherpurposes(HR6213).Washington.

/118/bills/hr6213/BILLS-118hr6213ih.pdf

.

9USCongress,HouseCommitteeonScience,Space,andTechnology.2023.HR6213-TheNationalQuantumInitiativeReauthorizationAct:Passedbyfullcommittee,November29.Washington.

/2023/11/the-national-quantum-initiative-reauthorization-act.

10USCongress.2023.NationalDefenseAuthorizationActforFiscalYear2024.January3.Washington.

/118/bills/hr2670/BILLS-118hr2670enr.pdf.

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feasibility,

11

Australia’se?ortstoutilizequantumcomputingfortransportationnetwork

optimization,

12

Japan’svisionofaquantumsociety,

13

andareportreleasedbytheCouncilofCanadianAcademiesthatidenti?edseveralindustries,includingtransportationandlogistics,thatcanbene?tfromquantumcomputing.

14

Inalignmentwiththepotentialforsigni?canteconomicimpactandimplicationsrelatedtoclimateandotherpolicies,itisimportanttoexplorekeyquestionspertainingtoquantumcomputinginthetransportationandlogisticsindustry:

●Whatareclassesofproblemsinlogisticsandtransportationthatmaybene?tfromquantumcomputing?Whatistherelevantscale?WhatistheanticipatedbusinessimpactofsuchQCsolution?Whatisthelikelihoodorfeasibilityofdeployingthis

solution?

●Whatisthestateofhigh-performancecomputingandQCinthelogisticsand

transportationsector?Forexample,isitcommonlyusedatscale,istheindustrywelleducatedonthetechnology,aretherestrongpartnerships?

●Whatconstraintsdologisticsandtransportationcompaniesfacewhenadopting

emergingtechnologies?Constraintsmightincludelowpro?tmargins,lagging

technology,highcomputingenergycosts,and/orlackofskills.WhichconstraintscanQCo?eringshelptoaddress?

Inseekingtoanswerthesequestions,expertsfromboththetransportationandlogistics

industryandthequantumcommunitiesconvenedataworkshoptoexplorethetypesof

quantumcomputingsystemsavailable,thealgorithmsandmethodstheyuse,theusecasesthattheyshowpromiseinaddressing,andthebusinessandgovernmentactionsthatcanbetakentoprogressthe?eldforward.

11GOV.UK.2023.FundingCompetition:FeasibilityStudiesinQuantumComputingApplications(February13–

March29).London

.uk/competition/1468/overview/3e95c2d9-

70ba-4a06-880f-c814422bb1f1.

12TransportforNewSouthWales.2021.QuantumTechnology.Sydney.

.au/system/?les/media/documents/2021/Transport%20for%20NSW%20and%20

Quantum%20Technology%20-%20WCAG%20version.PDF.

13SecretariatofScience,Technology,andInnovationPolicy.2022.VisionofQuantumFutureSociety.Tokyo.

https://www8.cao.go.jp/cstp/english//outline_vision.pdf.

14CouncilofCanadianAcademies.2023.QuantumPotential.Ottawa.

https://www.cca-

reports.ca/reports/quantum-technologies/.

Source:NationalInstituteofStandardsandTechnology

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DecisionMakinginSupplyChainswithQuantumComputing

Amajorityofthe83usecasesidenti?ed(72%)centeredaroundusingquantumcomputingtohelpwithdecisionmaking.Physicalsupplychainsareverycomplexwithmanyvariables,andunderstandinghowtomakegoodoperationaldecisionsishard.Thecombinationsof

movementforthousandsoftrucks,trains,planes,andshipsaredi?cultorimpossibleto

modelusingclassicalcomputing.Thus,mostoftheworkshop’sdiscussionscenteredaroundeitherproblemsthatseektooptimizeallocationofresources(e.g.,trucks,people)orthe

needtosimulatemarketdemandandre?ectthatsimulationonsupply(e.g.,trains,planes,labor).Theparticipantsalsodiscussedtheuseofmachinelearningto?ndpatternsin

performanceinformationinordertomakegoodorbetterdecisions.

Onlyafewoftheusecasesidenti?ednotedtheuseofquantumcomputingforresearch,

suchastodevelopbetterbatteries/powersourcesforvehiclesortominimizepollution,butthetimelinesforachievingthosewerefartheroutbecauseofthehardwareadvances

neededinQCtechnology.

Optimization.Asnoted,themajority(46ofthe83usecases,55%)oftheusecasesdescribedoptimizationasthekeyneed.Therewereseveralvariationsofoptimizationnoted.Many

focusedonaroutingproblem—?ndingthemoste?cientwaysformultiplevehiclesto

travelthatreducestraveltimesandmaximizescustomerservice.Variationsofrouting

included?ndingbetterschedulingtoolsformanufacturingande?cientroutingofvehicles

tominimizetra?ccongestion.Otheroptimizationsfocusedone?cientloadingand

unloadinginacomplexenvironment,suchasschedulingtheloadingandcreationofrailcarsandloadinganairplanetomaximizetheloadcarried.QuestionswereraisedaboutwhetheroptimizationcouldalsoapplytogovernmentcomplianceburdenssuchasloweringCO2

emissionsorexpeditingPFASremediatione?orts.

Simulation.Thenextmostpopularapproach(22of83,26%)intheidenti?edusecaseswas

simulation.Examplestendedtocenteraroundsimulatingorforecastingdemandthatinturnwouldallowtransportationassetstobeoptimizedtomeetthesimulatedforecast.Oneuse

casecalledforthecreationofafulldigitaltwin,i.e.,avirtualrepresentationofafactory,truck,train,etc.createdtosimulatedemandandsupplyplanning.

MachineLearning.Asmallernumberofusecases(11of83,13%)notedhowMLcouldhelp

withpatternrecognition,especiallyformonitoringvehicleperformanceformaintenanceandsafety.

Forafewusecasesforwhichoptimizationwastheprimaryapproachidenti?ed,experts

notedthatsimulationandMLwouldlikelyalsoplayarole,forexample,withassigning?eetsandcrewsandmanagingon-timedeliveries.

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UseCasesImpactandFeasibility

Weanalyzedthe83usecasesforquantumtechnologiesinlogisticsidenti?edbyexperts

andconsolidatedtheminto15examples(see

Figure2)

.Wefurtherrankedtheusecasesasthoseexpectedtohavethegreatestimpactandthosejudgedtobethemostfeasible.Theaverageimpactandfeasibilityscoresofeachusecasefromthisassessmentarepresentedinthegraphicsbelow.Thereisaclearlinearrelationship:Thefeasibilityandimpactofausecaseareoftensimilarlyranked,thoughthiscouldbecausedinpartbyanunconsciousbiaspeoplemaycarrythatthemorefeasibleusecasewillalsobethemoreimpactfulone.Still,weconcludefromthisassessmentthattherearekeyopportunitiesthatarerelatively

preparedtoleveragequantumtechnologyforlogisticalpurposes.

Theusecaserankedthemostfeasibleandimpactfuliscontinuousrouteoptimization,

makingitthebesttargetforresearch.Highimpactandhighfeasibilityimplylesstimeis

requiredtodevelopaworkingsolutionthanisrequiredforotherconcepts.Continuousrouteoptimizationisabroadconceptthatisrelevanttoalltransportationmodalities.Route

optimizationisanoldproblem,?rstdescribedbyLeonhardEulerinthe1700s,andresearchsinceWorldWarIIisextensive.

15

Therearealgorithmsforclassicalcomputerstosolve

routingproblems,butworkshopparticipantsnotedthattheproblemseithertaketoolongusingconventionalcomputingoraresimplytoobigforthecomputers,requiringenduserstoreducethenumberofvariablesorconstraintsinputtedintothemodel.

Theconceptratedthesecondmostfeasibleisoperatingplandesignandtrainscheduling.

Thisconceptreferstoaprocessofforecastingneedsfora?eet,includingthecrew,vehicles,andload,anddevelopingaplantomeettheneeds.Operatingplansarecomprehensive,

oftenconsideringfactorssuchastrainschedulingandrouting,traincarconstruction,cardistribution,and?eetmanagement.However,thisconceptwasratedonlythe?fthmostimpactful.Thisusecaseperfectlyillustratestheamountofcomplexitythatshouldbe

addressedinanoptimalsolution;routing,loading,personnelscheduling,maintenanceschedules,construction,weatherdisruptions,andmoreareallpossiblefactorstotrytoaccountfor.

15Euler,Leonhard.1741.Solutioproblematisadgeometriamsituspertinentis,CommentariiacademiaescientiarumPetropolitanae8:128–40.

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Figure1:Impactandfeasibilityofthe15consolidatedusecases

Figure2:The15consolidatedusecases,categorizedbytopic,typeofproblem,andrelevantquantum

technology

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Classi?cation

The83usecaseshavebeenclassi?edbyindustry,algorithmapproach(e.g.,optimization,ML,simulation),andareaofoperations(department).

Industry:Classi?cationbyindustryisgenerallyorganizedbymodeoftransportation(truck,train,plane,ocean),manufacturing,orpassengervehicleapplications.Justoverhalf(43of

83,51%)oftheusecasescouldbeappliedtoallindustries.Therestareidenti?edasspeci?ctoaportionofthesupplychain:16usecasesformanufacturing,10fortrucking,7for

passengervehiclesandcityapplications,4forretaillogistics,and3forrail.

Figure3:Numberofusecasesclassi?edineachindustry

Typeofproblem:Thedataareclassi?edbyproblemapproach.Theworkshopparticipants

wereaskedtouseoptimization,simulation,andmachinelearningastheirinitialchoices,andthisdefaultwassupportedbytheworkshop’sendresults:Thevastmajorityofusecasesareclassi?edprimarilyasanoptimizationproblem.

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Figure4:Numberofusecasesclassi?edineachprimarytypeofproblem

Area/departmentthatbene?tsfromaquantumsolution:Thedataarealsocategorizedby

departmentorareaofoperations.Congruentwiththefocusonoptimization,mostproblemsareinsomewayrelatedtotheplanningfunctioninacompany;only6usecasesare

problemslikelytobeassignedtoanIT/datateamandanother6wouldbeascribedtoagroupworkingonsustainabilityissues.

Fig