programmable nanophotonics for quantum information … · 2020-03-23 · programmable nanophotonics...

ProgrammableNanophotonicsforQuantumInformationProcessing and

Artificial Intelligence

DariusBunandar,NicholasHarris, DirkEnglundQuantumPhotonics Lab

CadencePhotonicsSummitSept.6th 2017

Acknowledgments

Funding

Prof.DirkEnglund (MIT)

Dr.NickHarris (MIT)

Prof.SethLloyd (MIT) Prof.MarinSoljačić (MIT)

Dr.TomBaehr-Jones (Elenion) Dr.MichaelHochberg (Elenion)

Outline

1. Programmablenanophotonic processor2. Photonicquantuminformation processing

3. Opticalneural network

Programmablenanophotonic processor

660µm

88MZIs,26inputmodes,26outputmodes,176phase shifters

ImagecourtesyofAFRL Rome

Thearrayin action

Stronglaserinput

Unitcell performance

>70dB visibility

Quantumsimulator architecture

Single-photonsources

Linearoptics unitary Single-photondetection

MichaelReck,AntonZeilinger,HerbertJ.Bernstein,andPhilipBertaniPhys. Rev. Lett. 73,58(1994)WRClements,PC Humphreys,B J Metcalf,WSKolthammer,IAWalmsleyOptica 3(12),1460-1465 (2016)

Unitcellperformanceasa qubit

Dualrail encoding

Randomized benchmarking

Photosynthesisandquantum transport

Environment-assistedquantumtransport (ENAQT)

P.Rebentrost,M.Mohseni,I.Kassal,S.Lloyd,A.Aspuru-Guzik,New Journal of Physics 11033003 (2009)

Noisyscattering simulations

Fully-integratedphotonicquantum computer

Heraldedsingle-photon sources

400µm

NicholasC.Harris,DavideGrassani,AngelicaSimbula,MihirPant,MatteoGalli,TomBaehr-Jones,MichaelHochberg,DirkEnglund,DanieleBajoni,ChristopheGallandPhys. Rev. X 4041047 (2014)

On-chipsingle-photon detectors

FarazNajafi*,JacobMower*,NicholasC Harris*,FrancescoBellei,AndrewDane,CatherineLee,XiaolongHu,PrashantaKharel,FrancescoMarsili,SolomonAssefa,KarlKBerggren,DirkEnglundNature Comm. 65873 (2015)

Incollaboration withProf.KarlBerggrenat MIT

Deeplearning

Artificialneural network

Canitbedonewith light?

Singularvalue decomposition

Opticalneural network

Vowelrecognition task

Powerinlog-spacedfreq. bands

90people speak4 vowels

Fouriertransform

360samples:180training+180 test

Thecaseforopticalneural network● Fast,low-energymatrixcomputation● Lowthermalnoise:goodforanalog encoding● NN’smoreresilienttoerrorsthangeneral-purpose computer

Equivalentcomputeperformanceof ONN:

R = m ×N2 ×BW FLOPS;m=layers,N xN matrixmultiplication,BW=bandwidth(>10 GHz)

Energy/FLOP Error propagation? Classification error

DigitalElectronic (GPU) ~100pJ/FLOP*, includingmemory retrieval

zero Low

Optical NN ~10/NfJ(signal re-gen)~10/(m×N)fJ (all-optical)

Betterthan10-bitprecisionforN=4096 with16-bitphase settings

Low(atleastforlowN<4096)

*M.Horowitz,Solid-StateCircuitsConferenceDigestofTechnicalPapers(ISSCC),2014IEEEInternational,10–14. IEEE.

Summary

1. Quantum simulation

2. Opticalneuralnetwork:potentialfornearlyenergy-freematrix multiplication

Outlook

● CMOSandphotonic integration

● Novelquantumphotonic devices○ Single-photon sources○ Single-photondetectors:Ge APDs○ MEMS integration High-Q

Output

Store-and-releasesinglephoton source

MikkelHeuck,MihirPant,DirkEnglund arXiv:1708.08875TaeJoonSeok,NielsQuack,SangyoonHan,RichardMuller,MingWuOptica 3(1)64-70 (2016)