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Fast Quantum Gate Control with Trajectory Optimization

dc.contributor.authorHu, Shouliangen
dc.contributor.authorLi, Mingen
dc.contributor.authorChen, Chunlinen
dc.contributor.authorDong, Daoyien
dc.date.accessioned2025-05-23T14:20:45Z
dc.date.available2025-05-23T14:20:45Z
dc.date.issued2024-07-01en
dc.description.abstractFast quantum control helps reduce the influence of unavoided disturbances and hence plays a vital role in practical quantum technology and chemical reactions. Instead of optimizing the terminal cost like standard optimal quantum control methods, this paper formulates the problem as a trajectory optimization problem, and implements the sequential quadratic programming algorithm to search for short control fields. The core idea is to minimize the cumulative intermediate error to incentivize early achievement of the designed gate. The numerical result on the Toffoli gate demonstrates the effectiveness of the proposed method.en
dc.description.sponsorshipCevaomlvpebwelhlil(e20sa1t7i)s;fyCinagnecvearteatinalc.o(n2s0t0ra9i)n)t.sA(ssesehDowefnfnienr Fanigd. Fig. 1. Fidelity landscape for a three-qubit quantum gate. evolve while satisfying certain constraints (see Deffner and Fig. 1. Fidelity landscape for a three-qubit quantum gate. 1, a quantum system cannot be controlled to evolve to the Fig. 1. Fidelity landscape for a three-qubit quantum gate. t1a,rageqtuastnattuemfassytsetremthacannanostpbeceifciocnttirmolel,edwthoicehvoislvreetfeorrtehde limheitr.eaFroerveaxriaomusplme,etBhoosdcsaitnoeesttimal.at(e2t0h1e4)qucahnatruamctesrpiezeeddTihge.r1e.aFreidvealirtiyoulasnmdescthapodesfotroaestthimreaet-equthbeitqquuaannttuummsgpaeteed. t1a,rageqtuastnattuemfassytsetremthacannanostpbeceifciocnttirmolel,edwthoicehvoislvreetfeorrtehde limit. For example, Boscain et al. (2014) characterized tttaooragsetthsetaqtueafnatttsuuutemmmrstpheaendalliimmspiiettcttiiifmmiceet..iPPmrreee,vvwiioohuuisschssttuuisddiireeessfehrarevvdee tihmeitt.imFeo-ropetxiammapl lter,ajBeoctsocraiiens efotratlw. o(-2l0e1v4el) qcuhaanratuctmeriszyesd-target state faster than a specific time, which is referred tihmeitt.imFeo-ropetxiammapl lter,ajBeoctsocraiiens efotratlw. o(-2l0e1v4el) qcuhaanratuctmeriszyesd-rtoevaesaltehdetqhuaatntthuemQsSpLeetdimliem, i\u03C4tQSLtim,eis. Parfeuvnidouams setnutdailebsohuanvde limit. For example, Boscain et al. (2014) characterized rtoevasealtheed thatquanthetumQSLspeedtime,limi\u03C4tQtSiLm,eis. Previousa fundamstudiesental boundhave thme stibmaes-eodptoinmtahletrPaojnecttroyraigeisnfMoraxtwimo-ulmevePlrqinucainptlue.mJosnyess- rreesvterailcetdedthbayt theQinStLritnismice,p\u03C4rope,rtisieas foufndthaemesynsttaelmbo(usnede the time-optimal trajectories for two-level quantum sys-rreesvterailcetdedthbayt theQinStLritnismice,p\u03C4rQSLope,rtisieas foufndthaemesynsttaelmbo(usnede and Kok (2010) gave a geometrical interpretation of the DeesftfrnicetredanbdyCtahmepinbterlilns(i2c01p7r)o)p.eMrtiaensdoelfsttahme saynsdtemTa(mseme anudanKtuomk (s2p0e1e0d) ligmavite. aIngpeoamrteicturilcaarl, iCnatenrepvraeteattiaoln. (o2f0t0h9e) restricted by the intrinsic properties of the system (see anudanKtuomk (s2p0e1e0d)ligmavite. aIngpeoamrteicturilcaarl, iCnatenrepvraeteattiaoln. (o2f0t0h9e) D1e9f4fn5e)rfirasntdfoCunamd pthbeelul n(c2e0r1ta7i)n).tyMrealnadtieolnstabmetwaenedn eTnaemrgmy first revealed the link between quantum dynamics and a1n9d45t)imfires,tgfoivuenndtahse\u25B3unHcer\u00B7ta\u25B3inTty\u2265rela\u210Fti,onanbdetwdeereinveednertghye(1945)firstfoundtheuncertaintyrelationbetweenenergy oiprsttimraelveaalgleodritthhmesl,inthkatbeist,wteheeneqffiucaienntucymofdythneamquicasntaunmd (1945) first found the uncertainty relation between energy oiprsttimraelveaalgleodritthhmesl,inthkatbeist,wteheeneqffiucaienntucymofdythneamquicasntaunmd axnpdretsismioen, ogfivQeSnL as \u03C4\u25B3QHSL =\u00B7 \u25B3T \u2265, wh\u210Fe,rea\u210Fndis dthereivreducthede optimal algorithm is governed by the intrinsic property of expression of QSL as \u03C4 = 2\u25B3\u03C0\u210FH, where \u210F is the reduced optimal algorithms, that is, the efficiency of the quantum ePxlapnrecsksiocnonosftaQnStLaansd\u03C4\u25B3H=is2\u25B3thHe, wsthaenrdea\u210Frdisdtehveiarteidounceodf the system. Caneva et al. (2009) utilized optimal control PthleanHckamciolntosntaiannt Han.dL\u25B3atHer, iMs atrhgeolsutsanadnadrdLedveitviinat(io1n998o)f mheethsyosdtsemto. Cesatnimevaateetthael. Q(2S0L09. )Inusttieliazdedoofpdtiimreactllyconotprtoi-l PthleanHckamciolntosntaiannt Han.dL\u25B3atHer, iMs atrhgeolsutsanadnadrdLedveitviinat(io1n998o)f methods to estimate the QSL. Instead of directly opti-tphreopHosaemdiltaonnoitahnerHQ. SLLateerx,pMreasrsgioonlu, sdaenfidneLdevaistin\u03C4QSL(1998=) meztihnogdsthteo eevsotliumtaiotne tthime eQTSL, .thIneystesaedt oaf fdixieredctvlyaluoeptTi-tproph\u03C0e\u210FHamiltonianosed anotherHQSL. Later,expression,Margolusdefinedand Levitinas \u03C4Q(S1L998=) mizingmethodstheto eestimatevolution thetimeQSL.T , theyInsteadset ofa fixeddirectlyvaluopti-e T prop\u03C0\u210F osed. another QSL expression, defined as \u03C4QSL = amnidzinimg ptlheemeenvoteludtitohne tKimroetoTv, atlhgeoyritshemt atofioxpedtimviazleuethTe prop22os.ed another QSL expression, defined as \u03C4QSL = andamizingnidziniimmgppthellheeemmeeennvotteeluddtithetohnetimeKKimrrooettooTvv,algorithmatheylhgeoyritsethemt atotofixedioptimizeoxpedtimviazleuethethTe \u03C0\u210F . aonndtriomlsp.lFemroemntaendinthiteiaKl sreottoovf Ta,lgthoreiythsmteatdoilyopretdimucizeed the 2. controls. From an initial set of T, they steadily reduced the \u22C622 vcoanluteroolfs.TFaronmd oabnseinrviteidaltsheet foafilTu,rethoefythsteeKadrioltyorveadlugcoerdiththme \u22C6 This work was supported by the Australian Research Council\u2019s value of T and observed the failure of the Krotov algorithm \u22C6FutureFellowshipfundingschemeunderProjectFT220100656andvatlaueceorftTainantdhroebssheorlvdedtrtahnesffearilutirmeeof\u03C4t.hTehKero\u2018ctoolvlaaplgseortiitmhme\u2019 Future Fellowship funding scheme under Project FT220100656 and aatlaueceorftTainantdhroebssheorlvdedtrtahnesffearilutirmeeof\u03C4t.hTehKero\u2018ctoolvlaaplgseortiitmhme\u2019 thThisTehNisatwwiooonrrakklwwNaaasstusuppsuraplportedoSrctieedncbbeyyFthetohuendAAauutssiottrrnaalloiiaafnnCRRheeinsseeaaarruccnhhdCouncil\u2019sCerouGnrcailn\u2019st \u03C4tisapcreortvaeinnttohrbeeshvoelrdytcrlaonssefetrottihmeet\u03C4h.eoTrheteic\u2018caollelastpismeattiemoe\u2019f FtheutuNationalre FellowsNaturalhip fundingScienceschemFoeuunderndatioPron ofjectChinaFT220100656under Grandant \u03C4tisapcreortvaeinnttohrbeeshvoelrdytcrlaonssefetrottihmeet\u03C4h.eoTrheteic\u2018caollelastpismeattiemoe\u2019f Future Fellowship funding scheme under Project FT220100656 and at a certain threshold transfer time \u03C4. The \u2018collapse time\u2019 the National Natural Science Foundation of China under Grant QSL 62073160. TQSLis p.roZvaehnedtionebjeadveerty acll.os(e20to14t)healtshoefooruetnidcatlheest\u2018icmoalltaepsoef 62073160. TQSL. Zahedinejad et al. (2014) also found the \u2018collapse 62073160. TQSL. Zahedinejad et al. (2014) also found the \u2018collapse 2405-8963 Copyright \u00A9 2024 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2024.08.358 awacceccoofommcppullsiissohhnfassetarqcuhainngtufmorccsoohnnottrrrtoollq..uFaaansstttuqqmuuaacnnottnuutmmrolcofinetldrosltios we focus on searching for short quantum control fields to caccrrrcuuucoccciiimaaalllpfffloooisrrrhpppfrrraaaascccttttiiiqcccuaaaalllnqqqtuuuuaaamnnntttcuuuommmnttreoclhhh. nnnFoooalllsoootgggqyyyu,, aaaansssttttuhhhmeee cccooohhneetrrreeonnlcciees tcirmuceiaolffoprrapcrtaicctailcaqluqbuitasntiusmlimteicthednoalongdy,uansatvhoeidceodhenroeinscees time of practical qubits is limited and unavoided noises htiimndeerof tphreacctoicnatlroqlubpietrsfoirsmliamnicteedwaitnhd aunlaovnogideqduannotiusems hinder the control performance with a long quantum ohpinedraertiotnhetimcoen. tTrhole qpuearfnotrummanspceeedwiltimh ita(QloSnLg) cqounasnidtuemrs ohpinedraertiotnhetimcoen. tTrhole qpuearfnotrummanspceeedwiltimh ita(QloSnLg) cqounasnidtuemrs hinder the control performance with a long quantum totthhhpeeeerammmtiaaaoxxxniiimmmtiumme. sssTpppheeedquaatntwumhicshpeaedqqqliuuumaaannnittttuuu(QmmmSLsssyyy)sssctttoeeenmmmsidccceaaarnnnsen
dc.description.statusPeer-revieweden
dc.format.extent6en
dc.identifier.issn2405-8971en
dc.identifier.otherORCID:/0000-0002-7425-3559/work/184100367en
dc.identifier.scopus85204294280en
dc.identifier.urihttp://www.scopus.com/inward/record.url?scp=85204294280&partnerID=8YFLogxKen
dc.identifier.urihttps://hdl.handle.net/1885/733752406
dc.language.isoenen
dc.relation.ispartofseries12th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2024en
dc.rightsPublisher Copyright: Copyright © 2024 The Authors.en
dc.sourceIFAC-PapersOnLineen
dc.subjectquantum gateen
dc.subjectsequential quadratic programmingen
dc.subjecttrajectory optimizationen
dc.titleFast Quantum Gate Control with Trajectory Optimizationen
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage336en
local.bibliographicCitation.startpage331en
local.contributor.affiliationHu, Shouliang; Australian National Universityen
local.contributor.affiliationLi, Ming; Guangdong University of Technologyen
local.contributor.affiliationChen, Chunlin; Nanjing Universityen
local.contributor.affiliationDong, Daoyi; School of Engineering, ANU College of Systems and Society, The Australian National Universityen
local.identifier.citationvolume58en
local.identifier.doi10.1016/j.ifacol.2024.08.358en
local.identifier.pure64024256-a20e-44df-88c5-c84972733ce6en
local.identifier.urlhttps://www.scopus.com/pages/publications/85204294280en
local.type.statusPublisheden

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