伯恩斯坦-全球半导体行业-科技未来：量子计算与量子处理单元的“台积电”Global Semiconductors Future of Tech Quantum Computing and the TSMC for QPU

FUTUREOFTECH18March2026

GlobalSemiconductors

FutureofTech:QuantumComputingandthe"TSMC"forQPU

DavidDai,CFA

+85229185704

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QingyuanLin

,Ph.D.

+85221232654

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MarkC.Newman

+12128457822

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CarmineMilano

+442077621857

ano@

FrancisMa

+85221232626

francis.ma@

ZhengCui

+85221232694

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AprilLi

+19173448339

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PhoebeSun

+19173448481

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QuantumProcessorUnits(QPUs)arelikelytobecomethenextimportantco-

processorindatacenters,sittingalongsideCPUsandGPUs.CPUswillremainthe

workhorseforgeneral-purposecomputing,whileGPUsdominatehighlyparallelworkloadssuchasAI.QPUs,inturn,couldbecomeessentialforasetofproblemsthataretoo

complexortoocostlyforclassicalprocessorstosolveefficiently.QPUsleveragequantummechanicstosolveexponentiallycomplexproblemsclassicalcomputerscannot.The

analogyisQPUscansearcha100-millionphonebookallatoncewhileCPUsgopagebypage.ForitsapplicationinAI,itsroleislimitedintheshorttermtohybridworkflows

suchasquantumkernels,featuremapping,andselectedoptimizationorsimulation-

ledtasks,butintheLT,whenlarge-scale,error-correctedquantumcomputersemerge,QPUscouldhelpacceleratepartsofmodeltraining,optimizationandpossiblygenerativemodeling.BeyondAI,theclearestapplicationsareindrugdiscovery,materialsscience,chemistry,andfinancialoptimization.

QPUscanbebuiltusingtwoleadingmodalities,superconductingcircuitsand

trappedions.SuperconductingQPUs,usedbyIBMandGoogle,relyonpatterned

Josephsonjunctioncircuitsfabricatedwithsemiconductorstyleprocesses,buttheleadingplayerskeepthisworkinhouse.SuperconductingQPUsbenefitfromfastgatespeedsandhighqubitcounts,butishighlysensitivetonoise.TrappedionQPUsusemicrofabricatediontrapchipswithpatternedelectrodes,plusgrowingopticalintegration,whilethequbitsthemselvesareindividualatomscontrolledbylasersandelectromagneticfields.This

technologyusesreal(ratherthansimulated)andhencehighqualityqubits,leadingtolongercoherencetimesandextremelyhighgatefidelity,butcomplexlaser/opticalcontrolsystemsmayhinderscaling.

Infineonistheleadingfoundryintrappedions,supplyingQPUtoQuantinuum

andIonQ.Itsroadmapistobuildtheenablinghardware-theiontraps,integrated

photonics,andcontrolelectronics,andpartnerwiththepure-playcompaniesmostlikelytoscaletrapped-ionsystemscommercially.IonQmaymovetoverticalintegrationwith

theacquisitionofSkyWater,butthatalsoleavessubstantialopportunitiestoInfineonforSkyWater’sothercustomers.IfQuantumcomputingtakesoffwithtrappedironparadigm,Infineoncouldbenefitsubstantially.

WebelievethatIBM’sinvestmentsinquantumcomputingwillyielddividends.IBM(IBM)hasspentthepastdecadeadvancingitsvisionoflarge-scalefault-tolerantquantumcomputingwithoneofthemostcomprehensiveroadmapsintheindustry.Onthehardwarefront,IBMdevelopssuperconductingqubitquantumprocessors,includingthe127-qubitEagle,133-qubitHeronr1,and156-qubitHeronr2andr3.Thesequantumprocessing

units(QPUs)arethenintegratedintoquantumsystemsanddatacenters.Onthesoftwareside,IBMoffersQiskit,theworld’sleadingopen-sourceplatformthatefficientlytranslateshigh-levelalgorithmsintoinstructionsoptimizedforquantumdevices.Bycombining

cutting-edgehardwarewithpowerfulsoftware,IBMQuantumdeliversafull-stackquantumcomputingsolutionaimedatmakingpracticalquantumapplicationsareality.

SeetheDisclosureAppendixofthisreportforrequireddisclosures,analystcertificationsandotherimportantinformation.Alternatively,visitourGlobalResearchDisclosureWebsite.

FirstPublished:18Mar202621:00UTCCompletionDate:18Mar202617:39UTC

DavidDai,CFA+85229185704

david.dai@

18March2026

BERNSTEINTICKERTABLE

17Mar

2026

TTM

AdjustedEPSReportedP/E(x)

Ticker

Rating

Cur

Closing Price

Price

Target

Rel.

Perf.

Cur2024A2025E2026E

2024A2025E2026E

IFX.GR(Infineon)

O

EUR

39.74

52.00

3.5%

EUR

1.38

1.65

2.33

51.0

39.5

23.4

688256.CH(Cambricon)

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1,066.00

2,000.00

23.3%

CNY

(1.09)

5.38

11.26

(978.0)

198.0

94.7

688041.CH(Hygon)

O

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231.40

280.00

23.2%

CNY

0.83

1.35

2.42

278.8

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IBM(IBM)

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256.11

330.00

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11.58

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24.8

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EDME

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ASIAX

1,723.55

SPX

6,716.09

O-Outperform,M-Market-Perform,U-Underperform,NR-NotRated,CS-CoverageSuspended

688256.CH,688041.CHestimateisReportedEPS;IBMvaluationisAdjustedP/E(x);IFX.GRbaseyearis2025;

Source:Bloomberg,Bernsteinestimatesandanalysis.

INVESTMENTIMPLICATIONS

IBM:werateIBMMarket-Perform,withatargetpriceof$330.

IFX:werateInfineonOutperform,withatargetpriceof€52.

Cambricon:werateCambriconOutperform,withatargetpriceofCNY2000.

Hygon:werateHygonOutperform,withatargetpriceofCNY280.

GLOBALSEMICONDUCTORSBERNSTEINlsocIeTecENeRAlECROUP2

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DETAILS

Quantumprocessorunits(QPUs)willrunsidebysidewithCPUsandGPUs.Inthepastdecade,wewitnessedtheriseofGPUs,thankstoAI.GPUsdoesnotreplaceCPUs;infact,majorityofthedaytodayworkisstillrunningonCPUs,e.g.checkingemail,browsing,workingonWordorExcelspreadsheets.ButGPUsareexcellentinonething-parallelmatrixcomputations,makingthemtheidealprocessorforAI.TheGPUhasbecomeanessentialco-processorofCPU.

Quantumprocessors(QPUs)havehighpotentialtobetheco-processorofthefuture.QPUsdon’treplaceCPUsorGPUs,asmostoftheclassicalproblemsarestilleasierorcheapertobeexecutedonCPUorGPUs.However,QPUsaresuperiorortheonlyoptionforasubsetofintractableproblems,makingthemirreplaceableinthefuture(Exhibit1,Exhibit2,Exhibit3).

Thisreportcontainstwomainparts.Inthefirstpart,wediscussthewayquantumcomputingworksanditspromisingfutureapplications.Inthesecondpart,wediscusshowtomakeaQPUusingtoday’ssemiconductorprocess,andhighlightthe

methods.

EXHIBIT1:CPUsremainbestforsequentialwork,whichmeanstasksthatmustbedonestepbystep.GPUsremainbestforhighlyparallelwork,whichmeansthesameoperationrepeatedmanytimesatonce.AQPU,ismorelikelytobecomeathirdtypeofacceleratorforanarrowerclassofproblemswheremanyinteractingpossibilitiesmust

beexploredtogether.

Source:Bernsteinanalysis

EXHIBIT2:GoogleWillow,theirstate-of-the-artquantumcomputingchipbasedonSuperconductingQubits

Source:Google

EXHIBIT3:MicrosoftMajorana1,theworld’sfirstquantumchippoweredbyTopologicalQubits

Source:Microsoft

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QUANTUMCOMPUTING:ANEWCOMPUTINGPARADIGM

Quantumcomputingleveragestheprinciplesofquantummechanicstoprocessinformationinwaysfundamentallydifferentfromclassicalcomputing.Whileclassicalcomputersrelyonbinarybits(0or1),quantumcomputersusequantumbits

(qubits).Iwon’tgetintothedetailsonthephysicsonwhatqubitsare(andBTWtherearemanytypesofqubits,GoogleWillow(GOOGL,Market-perform,coveredbyMarkShmulik)isbasedonsuperconductingqubits,whiletheMajorana1fromMicrosoftisbasedontopologicalqubits),butthe‘magic’hereisaqubitcanbeinastateof0,1,oranyprobabilisticcombinationofboth

simultaneously(Exhibit4).Youmayfeelabitdifficulttoimagingwhatthismeans,becausethisquantumphenomenondoesnotexistinthedailyenvironmentthatwearefamiliarwith,butone(notsoaccurate)analogyhereforqubitisacointhatisspinningintheair,untilyoucatchitthecoinisinacombinationoffacingbothupsideanddownside.ThisiscalledSuperposition

(Exhibit5),whichisthecorequantumphenomenonthatenablesaquantumcomputertoperformparallelcomputationson

multiplestatesatonce.AnothercorephenomenoniscalledEntanglement(Exhibit5):Whenqubitsbecomeentangled,the

changeinstateofonequbitinstantaneouslyinfluencesthestateofanother,evenacrossvastdistances.Combiningthetwo

createscorrelatedsystemsthatexponentiallyincreasecomputationalpowerasmorequbitsareadded,inotherwordsmakingcalculationmuchfasterforcertaintasks.Forexample,whenwetrytosearchonephonenumberina100-millionphone

book,aclassicalcomputercheckseachpageoneatatime,whileaquantumcomputerchecksallpagesatthesametime.Thedifferenceincomputingtimeconsumptionthusisexponentiallydifferent.IntheexampleofWillow’sperformance,theyusedtherandomcircuitsampling(RCS)benchmark,Willowperformedacomputationinunderfiveminutesthatwouldtakeoneoftoday’sfastestsupercomputers1025or10septillionyears.Asareference,ouruniverseisonlyapproximately13.8billionyearsold,or1010years,sothat’satimespanthatismuchlongerthanthelifeofouruniverse.

EXHIBIT4:Illustrationofclassicalbitvsqubit

EXHIBIT5:IllustrationofQuantumentanglementandQuantumsuperposition

Source:

/figure/Quantum-entanglement-and

-quantum-superposition_fig1_350597652

Source:

/Terminology/Difference-between

-

Bit-and-Qubit.html

Althoughthissoundsverycool,wewanttohighlightthatQuantumcomputingandclassicalcomputingarefundamentally

differenttoolsdesignedtosolvedistinctclassesofproblems.Theyarecomplementaryratherthancompetitive.Quantum

Computersarespecializedinsolvingspecific,complexproblemsthatinvolvedexponentialcomplexitythatareintractablefor

classicalcomputersduetotheirsequentialprocessinglimitations,buttheyarenotgoodatdeterministic,everydaytasksthat

weneedinourday-to-daylife,forexamplesendingemails,browsingwebsites,orstreamingvideos.Inaddition,quantumstatesareinherentlyfragileandthusquantumcomputerscannotbemovedaround,mostquantumsystems(e.g.,superconducting

qubits)requirenear-absolute-zerotemperatures(~10mK)andultra-stableelectromagneticshielding,makingitimpossibleatthispointtominiaturize.Exhibit6,Exhibit7showstwoexamplesbyGoogleandMicrosoft,eventhoughthechipissmallenoughthatyoucanholditonyourhand,alltheotherelementsforcooling,shielding,electricconnectionetc.needtotakeoneormanybigroomstohost,andwedon’tforeseeinthenearfuturethissituationwouldchange.Thismeansthatevenifthescientists

canbuildanoperationalquantumcomputer,yourlaptopisn’tbecomingquantum-powered,butyourcloudservicesmight

tapquantumcomputersforspecializedtasks.Theanalogyhereofthecomparisonbetweenquantumcomputerandclassicalcomputerisarocketvsacar,therocket(analogytothequantumcomputers)couldbethousandsoftimesfasterthanthecar(analogtotheclassicalcomputers),buttheyarejusttwoverydifferentkindsofvehiclesthatyouwillneverreplaceallcarswithrockets.

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EXHIBIT6:ImageofthecoolingelementsofGoogle’squantumcomputer

Source:Google

EXHIBIT7:ImageofthecoolingelementsofMicrosoft’squantumcomputer

Source:Microsoft

RECENTDEVELOPMENTINQUANTUMCOMPUTING

Theearlystageofquantumcomputingdevelopmenthasremainedmainlyonbuildingmorephysicalqubits,thesesystems

tendtobestillnoisywithhigherrorrates.In2019,GoogleclaimedtohaveachievedquantumsupremacywithSycamore(53

physicalqubits),solvingataskin200secondsthatwouldtakeasupercomputer10,000years,commonlycitedasamilestoneinquantumcomputingresearch.However,thetaskthatGooglesolvedisnotpracticallyuseful(samplingtheoutputofarandomquantumcircuit),andsomeresearchersarguedthatclassicalcomputersmightbeabletosolvesimilarproblemsmoreefficientlywithfurtheradvancements.

In2023,IBM’sreleaseofthefirst1,000qubitquantumchip,the1,121-qubitCondor,wasanimportantscalingmilestonebecauseitshowedthatIBMcouldpushmonolithicscalingtoamuchlargersuperconductingprocessor.However,IBMmadeclearitsperformancewasdescribedascomparabletotheprevious433-qubitOsprey,underliningthatphysical-qubitcountaloneisnotasufficientmeasureofprogress.Inquantumcomputing,whatmattersisnotjustscale,butwhetherthatscalecomeswithhighgatefidelity,lowcrosstalk,stablecontrol,anderrorrateslowenoughtosupportquantumerrorcorrection.

ThatiswhyIBM’sHeronprocessorspresentedinthesameevent(Exhibit8)aregenerallyseenasmoremeaningfulthanCondorfromaperformanceperspective.WhilethefirstHeronhadonly133qubits,farfewerthanCondor’s1,121,IBMdescribesHeronasitshighest-performingprocessorfamily,builtwithtunablecouplers,deliveringa3–5ximprovementindeviceperformance

overEagleandvirtuallyeliminatingcrosstalk.

GLOBALSEMICONDUCTORSBERNSTEINlsocIeTecENeRAlECROUP6

EXHIBIT8:In2023,IBMreleasedtheHeronquantum

chip,with133quibits,deliveringa3–5ximprovementindeviceperformanceoveroldergenerationsandvirtuallyeliminatingcrosstalk.

Source:IBM,Bernsteinanalysis

Quantumcomputationworksbyapplyinggatestoqubitstocreatehighlycorrelatedquantumstates,thenmeasuringthemtoproduceanoutput.Foryears,themaindoubtwaswhetherthiscouldscale,becausequbitsnaturallyloseinformationovertimeandtheveryoperationsusedtocontrolandreadthemalsointroduceerrors.

Inthisregard,anotherimportantrecentbreakthroughisthatteamsatGoogleQuantumAI,Quantinuum(private),Harvard/

QuEra(private),andtheUniversityofScienceandTechnologyofChina(USTC)havenowshownthattheseerrorscanbe

managedthroughQuantumErrorCorrection(QEC).InQEConelogicalqubitisencodedacrossmultiplephysicalqubits,sothesystemcandetectandfixdegradationduringcomputation.Crucially,thesegroupsdemonstratedthattheirerrorratesarebelowthethresholdneededforrepeatedcorrectiontoimprovereliabilityratherthanworsenit,whichiswhymanyphysicistsnowseelarge-scale,fault-tolerantquantumcomputingasaviablepath.

Google’sWillowquantumchip,unveiledinlate2024,showedthaterrorratescandropexponentiallyastheencodedsystem

grows,akeyrequirementforscalablequantumerrorcorrection(Exhibit9,Exhibit10).ThisiswhyWillowisoftenviewedasoneofthemostsignificantrecentsuperconductingchips:itadvancedthefieldonthemetricthatmattersmostforfaulttolerance—whetheraddingqubitshelpssuppresserrors,ratherthansimplyaddingmorenoise.GooglepositionedWillowasabridge

betweenearlybeyond-classicalexperimentsandthemorechallenginggoaloflarge,error-correctedquantummachinescapableofrunningcommerciallymeaningfulalgorithms(Exhibit11).

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EXHIBIT9:Googledemonstratedthatlogicalqubit

performanceimprovesassurface-codesizeincreases.Movingfrom3×3(red)to5×5(cyan)and7×7(blue)

significantlyreduceslogicalerrorrates.OnWillow,the7×7logicalqubitremainscoherenttwiceaslongasthebestphysicalqubit(green)andtwentytimeslonger

thantheprevioussurface-codeimplementationonSycamore(gray,black)

EXHIBIT10:Schematicofadistance-7(d=7)surfacecodeona105-qubitprocessorproposedbyGoogle,consist

ofmeasurequbit(blue,7x7),stabilizer(blue-colouredtile),dataqubits(gold,7x7)andleakageremovalqubits(green)

Source:

/articles/s41586-024-08449-y

Source:Google,Nature,Bernsteinanalysis

EXHIBIT11:Google’squantumcomputingroadmap

Source:Google,Bernsteinanalysis

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QUANTUMCOMPUTINGFORAIANDOTHERAPPLICATIONS

QuantumcomputingforArtificialIntelligence

QuantumcomputinghasgainedmomentumasapotentialenablerforAI,particularlyinmachinelearning,becauseboththe

needfornewcomputeapproachesandtheunderlyingquantumtechnologyhaveadvancedmaterially.TherecentAIwavehasexposedgrowingbottlenecksincompute,memory,andpower,whilequantumcomputinghasmaturedfromalargelytheoreticalfieldintoonewithamorecrediblealgorithmicbase,andincreasinglyvisiblehardwareroadmaps.

Additionally,theecosystemisincreasinglymovingfromtheorytoexperimentation,withlargeplayersnowbuildingconcrete

AI–quantumplatforms:Japan’sAISTisworkingwithIonQ(IONQ,notcovered)andQuEra(private)onABCI-Qasahybrid

environmentforquantumsimulation,andquantummachinelearning;NvidiahaslaunchedtheNVAQCtoconnectquantum

hardwarewithAIsupercomputinginfrastructure(Exhibit12);SoftBankandQuantinuumaretargetingpracticalAI–QC

applicationsthroughpartnershipwithQuantinuum’sGenQAIframeworkwhichisexplicitlydesignedtobringquantum-

generateddataintoAIworkflows(Exhibit13).Together,theseexamplesshowthattheAI–QCstoryisnolongerjustconceptual,butisbeginningtotakeshapeinrealinfrastructure,software,andapplicationdevelopment.

EXHIBIT12:NVIDIAlastyearannounceditisbuildingtheNVIDIAAcceleratedQuantumResearchCenter(NVAQC)integratingquantumhardwarewithAIsupercomputers,enablingacceleratedquantumsupercomputing.

EXHIBIT13:SoftBankandQuantinuumannounceda

partnershipinquantumcomputingtoovercomethelimitationsofclassicalAIandrealizenext-generationtechnologies.

Source:Quantinuum,Bernsteinanalysis

Source:NVIDIA,Bernsteinanalysis

QuantumAIintheshort/mediumterm

Intheshort/mediumterm,meaningontoday’sNISQ(noisyintermediatescalequantum)hardware,theroleofquantum

inAIisnarrow.Oneoftheclearestexampleisinquantumkernelsandrelatedfeature-mappingtasks.Akernelisawayof

measuringsimilaritybetweendatapointsaftermappingthemintoarichermathematicalspace.Thequantumprocessor

helpstransformdataintoaricherrepresentationandaclassicalmodelstilldoesmostofthelearning.Inpracticalterms,

thismeansquantumismostlikelytohelpwithselectedsub-taskssuchasclassification,similaritysearch,orparts

ofoptimizationratherthanwithend-to-endAIsystems.In“QuantumMachineLearninginFeatureHilbertSpaces”,2019,

SchuldandKilloranexplainthataquantumcomputercannaturallyperformthiskindofmappingintoaverylargefeature

space.Beyondcoremachine-learningtasks,themostcredibleearlyAI-adjacentvalueislikelytoemergefromhybridAI–QC

workflowsinsimulation-andoptimization-intensivedomains,whereAIisalreadyusedalongsidescientificcomputingincludingdrugdiscovery,materialsmodeling,catalystdesign,andfinancialriskanalysis—ratherthanasastandaloneendapplication.

Wediscusstheseapplicationsinmoredetailbelow.RigettiComputing(RGTI,NotCovered)CEO,Dr.SubodhKulkarniatthe2026TMTForumalsobelievesthatthefirstmeaningfulAI-adjacentvaluefromQCismorelikelytocomefromhybridAI–QCworkflowsinsimulationandoptimizationthanfrommainstreamenterpriseAI.

Hence,theliteratureandrecentindustrycommentarydonotsupporttheideathatcurrentquantumhardwarecanmaterially

acceleratemainstreamAItraining.Onemajorreasonisdataloading:ifthedatabegininclassicalform,movingtheminto

quantumstatescanbeslowandcanerasemuchofthetheoreticalspeedup.Therefore,therealisticshorttomediumtermconclusionisthatQCcanhelpAIinalimitednumberofhybridapplications,especiallyfeaturemapping,optimization

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andsimulation-ledworkflows.

QUANTUMCOMPUTINGFORAIINTHELONGERTERM

Thelongertermcaseismoreinterestingandalsomorespeculative.Intheory,quantumcomputingofferstransformative

potentialtoaccelerateartificialintelligencethroughexponentialparallelisminprocessinghigh-dimensionaldatasets.The

quantum-enhancedmachinelearningtechniqueslikequantumneuralnetworkscanbedesignedtohavepolynomial-to-

exponentialspeedupsintrainingdeeplearningarchitecturesandfeatureextractiontasks.Thestrongestlongtermideaisthatquantumcouldspeeduppartsofoptimizationandlinearalgebrathatsitunderneathmachinelearning.Optimizationmeanstherepeatedprocessofadjustingmodelparameterstoreduceerror.InTowardsprovablyefficientquantumalgorithmsforlarge-scalemachine-learningmodels,2023,Liuetal.showthatfault-tolerantquantumcomputingcould,underspecificassumptions,accelerateversionsofstochasticgradientdescent,whichisoneofthecentralworkhorsesofmodernmachinelearning.Thisisimportantbecauseitdirectlylinksquantumcomputingtothetrainingloopoflargemodels.

Asecondlongertermareaisgenerativemodeling,whichmeanslearningaprobabilitydistributionandthengenerating

newsamplesfromit.ThismattersbecausemodernAIreliesheavilyonprobabilisticmodels.In“Aquantummachinelearningalgorithmbasedongenerativemodels”,2018,Gaoetal.arguethatquantumgenerativemodelscanrepresentsome

distributionsmoreefficientlythanclassicalonesandcanofferexponentialspeedupinlearningandinference.Thisis

conceptuallyimportantbecauseitsuggeststhatquantummayeventuallymatternotonlyforfastermathunderthehood,butalsofortheactualmodelingofcomplexsystems(Exhibit14).Again,thelimitingfactorishardware.Withoutstableanderrorcorrectedmachines,thisremainsapromisingdirectionratherthanacommercialnear-termapplication.

EXHIBIT14:Apossibleapproachforlarge-scalemodelsistopre-trainadensenetworkclassically,prunemost

oftheweights,andthenusetheremainingsparsenetworktobuildaquantumdifferential-equationsystem

basedonthetrainingdata.Ifthatsystemissufficientlysparseanddissipative,quantummethodsmayofferanadvantageintheearlystageoftraining.Thesolutionisthenmeasuredtorecoverthetrainedparametersand

reconstructaclassicalsparseneuralnetwork

Source:"Towardsprovablyefficientquantumalgorithmsforlarge-scalemachine-learningmodels"J.Liuetal.2023

OTHERPOTENTIALAPPLICATIONS

BeyondAI,themostcredibleearlyapplicationsforquantumcomputingarescientificandindustrialworkloadswhereclassicalcomputerseitherstruggletosimulatequantumsystemsaccuratelyorbecometooslowwhensearchingverylargeoptimizationspaces.

CryptographyandCybersecurity:Mostofmodernencryption(suchasonlinebanking,blockchain,andgovernment

communications)arebasedonawidelyusedencryptionmethodcalledRSA.Itisconsideredverysafeuntilthetheoryofquantumcomputingcomesout,becausedecryptinga2048-bitRSAnumberwouldtakeeventhefastestsupercomputers

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millionsofyears.AmathematiciancalledPeterShordevelopedanalgorithmexploitsquantummechanicstoperformdecryptionexponentiallyfasterthanclassicalmethods,ifyouhaveaquantumcomputerwith~4000logicalqubits(whichmeansmillionsofphysicalqubits)youwillbeabletocrackitinoneday.Althoughyouprobablydon’tneedtoworrytoomuchatthispoint,becausetodaythemostcutting-edgequantumcomputershaveonly~100physicalqubitsandisstillverydifficulttomake.Atthesametime,manyothertechnologiesarebeingdevelopedtocreateunhackablecommunicationchannels,asdemonstratedinChina’sMiciussatelliteexperimentsusingQuantumKeyDistribution(QKD).

Drug&MaterialsDiscovery:Quantumcomputerscanmodelmolecularinteractionsatthequantumlevel,bypassing

approximationsrequiredbyclassicalmethods.Forexample,simulatingcaffeine(C₈H₁₀N₄O₂)wouldtakeclassical

supercomputersmillenniabutcouldbedoneinhoursonafault-tolerantquantumcomputer.ThiscouldacceleratedrugdiscoveryfordiseaseslikeAlzheimer’s,andcouldenablenewmaterialsdiscovery,suchasmaterialsforhigh-temperaturesuperconductors.

SupplyChainOptimization:ManymodernsupplychainoptimizationsuchasRoutePlanningandInventoryManagement

couldbenefitfromquantumcomputing.Thecompute