The he fut uture ure of of comput omputing: ng: Quant uantum - - PowerPoint PPT Presentation

the he fut uture ure of of comput omputing ng quant
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The he fut uture ure of of comput omputing: ng: Quant uantum - - PowerPoint PPT Presentation

The he fut uture ure of of comput omputing: ng: Quant uantum um Andr ndrs s Gil Gilyn yn PhD PhD: Postdoc: Postdoc: The he fut uture ure of of comput omputing: ng: Quant uantum um -Our -Our wor orld is qu ld is


slide-1
SLIDE 1

The he fut uture ure of

  • f comput
  • mputing:

ng: Quant uantum um

Andrá ndrás s Gil Gilyén yén PhD PhD: Postdoc: Postdoc:

slide-2
SLIDE 2

The he fut uture ure of

  • f comput
  • mputing:

ng: Quant uantum um

  • Our
  • Our wor
  • rld is qu

ld is quan antum tum m mec echani anical. al.

  • Qua
  • Quantum

ntum com compute puters rs e ena nable ble no novel el c com

  • mpu

putat tations. ions.

slide-3
SLIDE 3

Quant uantum um ef effect ects for

  • r com
  • mput

uting ng

  • Sup
  • Superpo

erpositio sition: n: a qu qubit bit can can be be both both 0 0 an and d 1 1 simu simultane ltaneou

  • usly (with

sly (with some some ampl amplitud itudes) es)

  • Int
  • Interferen

rference: ce: co computatio mputations ns in in sup superp erpositio

  • sition

n can can co coll llect ectivel vely y co contrib ntribut ute to to the fin the final resu al result lt

  • Entan
  • Entangle

glemen ment: : qu qubi bits s can can ha have ve st strong

  • nger

er than an cl classica assical corre l correlatio lations ns

slide-4
SLIDE 4

Quantum supremacy Quant uantum um supr suprema emacy

  • Quantum computers have the potential to solve
  • Qua
  • Quantum

ntum compute puters rs hav ave th the e pote

  • tenti

ntial al to to solve

  • lve

some problems exponentially more efficiently than some me pr probl

  • blem

ems ex expo pone nentia ntially lly more

  • re effic

ficient ently ly tha han n classical computers. clas assical com al compu puters. ters.

  • Google just reported passing the cross-over
  • Goo
  • Google

gle jus just rep report

  • rted

ed pas passing sing th the e cros cross-ov

  • ver

r point, where a quantum chip can be much faster po poin int, t, wher where e a qu quant antum um chip ip can can be be muc uch fa faste ter r in practice than the best available supercomputer. in in pr prac actice t tice than han the the b bes est t av avai ailab lable le sup uperc ercomp mpute uter.

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SLIDE 5

Boaz Barak’s analogy (quoted by Scott Aaronson) Boa Boaz B Barak’ rak’s a anal nalog

  • gy (qu

(quote

  • ted b

d by Sc Scott

  • tt Aaron

ronson

  • n)

vs. vs.

slide-6
SLIDE 6

Boaz Barak’s analogy (quoted by Scott Aaronson) Boa Boaz B Barak’ rak’s a anal nalog

  • gy (qu

(quote

  • ted b

d by Sc Scott

  • tt Aaron

ronson

  • n)

vs. vs. “ “Deep Blue vs. Deep Deep B Blue vs ue vs. . Kasparov” Kas Kasparo arov”

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SLIDE 7

Mai ain n tec echni hniques ques for

  • r quant

quantum um al algori gorithm hms

  • Quant

uantum um Fo Four urier trans nsfor

  • rm: Shor

hor’s s al algor gorithm for

  • r

fact ctor

  • ring,

ng, br breaki aking ng RSA cr crypt ypto-

  • -sys

system em, etc.

  • Ham

amiltoni

  • nian

an si simul ulation:

  • n: dynam

dynamical al si simulat ation

  • n of
  • f

quan antum um syst ystems for

  • r chem

hemist stry, mater erial sc scienc ence, e, etc. c.

  • Grover
  • ver sear

search: ch: gene neric c quadr quadrat atic sp spee eed-up up for unst struct ctur ured ed sear search ch pr problem ems

  • Lar

Large- ge-di dimen ensional

  • nal

regr gres essi sion

  • n

(HHL al algo gorithm): speedi speeding- ng-up up var various

  • us machi

hine ne lear earni ning ng appl applicat ations

  • ns
slide-8
SLIDE 8

Qua uantum um Si Singul ngular ar Val alue ue transf ansfor

  • rmat

ation

  • n
  • A common
  • n uni

unificat cation

  • n / gener

general alizat ation

  • n of
  • f Ham

amiltoni

  • nian

an si simul ulat ation,

  • n, Grov
  • ver

er sear earch and and regr egress ession

  • n (HHL)

L).

  • Block
  • ck-enc

encodi

  • dings:

ngs: exponent exponential ally fast aster er mat atrix oper

  • perat

ations

  • ns
  • Efficient

ent ci circui uits & ne near-ter erm appl applicabi cability

slide-9
SLIDE 9

Some e appl applicat cations

  • ns / ot
  • ther

er res resul ults

  • Speedin

ing up gradie ient t co computa tatio tion usin ing quantu tum compute ters with with applic icatio tions to to variatio tional cir ircuits its and quantu tum neural netw tworks.

slide-10
SLIDE 10

Some e appl applicat cations

  • ns / ot
  • ther

er res resul ults

  • Speedin

ing up gradie ient t co computa tatio tion usin ing quantu tum compute ters with with applic icatio tions to to variatio tional cir ircuits its and quantu tum neural netw tworks.

  • Speedin

ing up Lin inear Programs, , Semidefin finite ite Programs, , and general l co convex opti timiz izatio tion problems s + fin findin ing lim limita itati tions s on quantu tum s speed-ups. s.

slide-11
SLIDE 11

Some e appl applicat cations

  • ns / ot
  • ther

er res resul ults

  • Speedin

ing up gradie ient t co computa tatio tion usin ing quantu tum compute ters with with applic icatio tions to to variatio tional cir ircuits its and quantu tum neural netw tworks.

  • Speedin

ing up Lin inear Programs, , Semidefin finite ite Programs, , and general l co convex opti timiz izatio tion problems s + fin findin ing lim limita itati tions s on quantu tum s speed-ups. s.

  • Effi

ficie iently tly wo working with th th the lo lowest- t-energy sta tate tes of f some str tructu tured H Hamil ilto tonia ians ( s (quantu tum m mechanical s l syste stems)

slide-12
SLIDE 12

Some e appl applicat cations

  • ns / ot
  • ther

er res resul ults

  • Speedin

ing up gradie ient t co computa tatio tion usin ing quantu tum compute ters with with applic icatio tions to to variatio tional cir ircuits its and quantu tum neural netw tworks.

  • Speedin

ing up Lin inear Programs, , Semidefin finite ite Programs, , and general l co convex opti timiz izatio tion problems s + fin findin ing lim limita itati tions s on quantu tum s speed-ups. s.

  • Effi

ficie iently tly wo working with th th the lo lowest- t-energy sta tate tes of f some str tructu tured H Hamil ilto tonia ians ( s (quantu tum m mechanical s l syste stems).

  • Using quantu

tum machin ine le learning ideas s to to speed up classic ical machin ine le learnin ing ta tasks.

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SLIDE 13

I woul

  • uld

d like e to

  • thank

hank my my wonderf

  • nderful

ul co-aut

  • -author

hors. s. Especi pecial ally, my my PhD hD adv advisor

  • r Rona

nald d de de Wolf, for

  • r int

ntroduc roducing ng me me to

  • thes

hese e fasc ascina nating ng topi

  • pics

cs and and gui guidi ding ng me me throughout hroughout my my PhD PhD years ears.

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SLIDE 14

* sourc source e of

  • f images

ages:

  • Pi

Pint nter erest est

  • Goo
  • ogle
  • IBM

BM / digi gital altrends. ends.com

  • m
  • Wikiped

edia