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Frontiers in Optics

Quantum Optics on Photonic Chips

Dirk Englund

Published Jan 1, 2011• 0 citations
Publisher

Abstract

This talk will describe controlling light matter interaction in photonic nanostructures aids the construction of basic components for quantum information science, and how complex optical functions can be translated from bulk optical setups into the photonic integrated chip (PIC) architecture. Article not available.
DOI
10.1364/ls.2011.ltuf3

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Integrated micro- and nano-structures allows for the efficient generation of photon pairs via parametric fluorescence, thanks to the enhancement of the light-matter interaction associated with light confinement in small volumes. For instance, the efficiency of spontaneous four wave mixing in a silicon micro ring resonator can range up to 10 orders of magnitude larger than in bulk silicon. Yet the advantages of using integrated devices go well beyond the sole efficiency improvement, for micro structures grant an unprecedented control over the properties of generated non-classical light. For instance, in a single photonic integrated circuit, one can exploit the interferences of many optical elements to construct complex multipartite states in a compact, stable, and scalable system. In this talk I will focus on the generation of photon pairs and show how their spectral properties can be engineered in an integrated device for use as building blocks in the construction of a whole variety of states, from heralded single-photons to multipartite states.
Recent advances in materials, control, and nanofabrication now open the prospect for scalable quantum technologies based on solid-state quantum systems. In particular, photonic integrated circuits (PICs) now allow routing photons with high precision and low loss, and solid-state artificial atoms provide high-quality spin-photon interfaces. The first part of this talk will review progress towards quantum memory-integrated PICs for quantum networks and modular quantum computers. The second part of the talk will consider new directions for processing classical and quantum information in deep learning neural networks architectures.