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DTSTAMP:20260424T040530Z
UID:62c694389c844604746045@ist.ac.at
DTSTART:20220708T100000
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DESCRIPTION:Speaker: Andrew White\nhosted by Johannes Fink\nAbstract: There
  is now an enormous opportunity to interconnect quantum components togethe
 r into complex\, short- and long- range networks of sensing\, communicatio
 n\, and computational elements. Photons are a natural choice for networkin
 g quantum technologies as their quantum nature survives at room temperatur
 e and long distance propagation is possible\, either via optical fibre or 
 through free space.Here we explore using machine learning (ML) to optimise
  production\, coupling\, routing\, and circuitry for single photons. Our s
 ingle-photon source platform is resonant excitation of individual quantum 
 dots coupled to a micropillar cavity. Multiphoton suppression in the quant
 um dot emissionas well as single-photon indistinguishability and brightnes
 sare directly influenced by the spatiotemporal characteristics of the opti
 cal excitation pulses. We use ML techniques to tailor the excitation laser
  pulse properties in real-time\, significantly reducing the search time fo
 r optimal parameters. We also employ ML to control a deformable mirror\, c
 orrecting for aberration on the single-photon wavefront field to maximise 
 the coupling between the source output and a single-mode fibre. This combi
 nation provides a toolbox for enhancing the performance of any solid-state
  single-photon source.Photonic integrated circuits (PICS) will be essentia
 l for scalaby realising photonic quantum technologies. Actively coupling p
 hotons into PICS requires high-fidelity integrated switches. Current best 
 practicemanual optimisation of electronic signals for each individual swit
 ch on a chipis slow and unscalable. We use MLsimulated annealingto optimis
 e driving parameters for up to 4 switches on a single chip\, achieving a s
 ignificant speed up in tuning while retaining optimal performance. PICS of
 ten interface light in and out of the chip using edge coupling\, which sev
 erely limits chip geometry as well as adding complication to fabrication. 
 Using MLinverse designwe are developing efficient out-of-plane couplers an
 d small-footprint waveguide crossings that are easier to manufacture and h
 ave higher circuit density. This new architecture lowers entry costs for p
 hotonic integrated circuitry development.
LOCATION:Mondi Seminar Room 2\, Central Building\, ISTA
ORGANIZER:swiddman@ist.ac.at
SUMMARY:Andrew White: Rise of the Machines: Making better photons by gettin
 g rid of experimentalists
URL:https://talks-calendar.ista.ac.at/events/3861
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