Integrated quantum photonics

Towards an Integrated Quantum Photonics Platform on GaAs

Frontiers in Optics 2016 ◽

10.1364/fio.2016.fw5d.4 ◽

2016 ◽

Author(s):

Sven Höfling

Keyword(s):

Quantum Photonics

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422 Million intrinsic quality factor planar integrated all-waveguide resonator with sub-MHz linewidth

Nature Communications ◽

10.1038/s41467-021-21205-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Matthew W. Puckett ◽

Kaikai Liu ◽

Nitesh Chauhan ◽

Qiancheng Zhao ◽

Naijun Jin ◽

...

Keyword(s):

Quality Factor ◽

High Capacity ◽

Precision Spectroscopy ◽

Narrow Linewidth ◽

Waveguide Resonator ◽

Environmental Disturbances ◽

Fiber Communications ◽

Intrinsic Quality ◽

Quality Factor Q ◽

Quantum Photonics

AbstractHigh quality-factor (Q) optical resonators are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration in a photonic waveguide platform is key to reducing cost, size, power and sensitivity to environmental disturbances. However, to date, the Q of all-waveguide resonators has been relegated to below 260 Million. Here, we report a Si3N4 resonator with 422 Million intrinsic and 3.4 Billion absorption-limited Qs. The resonator has 453 kHz intrinsic, 906 kHz loaded, and 57 kHz absorption-limited linewidths and the corresponding 0.060 dB m−1 loss is the lowest reported to date for waveguides with deposited oxide upper cladding. These results are achieved through a careful reduction of scattering and absorption losses that we simulate, quantify and correlate to measurements. This advancement in waveguide resonator technology paves the way to all-waveguide Billion Q cavities for applications including nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications.

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Quantum Reinforcement Learning with Quantum Photonics

Photonics ◽

10.3390/photonics8020033 ◽

2021 ◽

Vol 8 (2) ◽

pp. 33

Author(s):

Lucas Lamata

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Quantum Computation ◽

Exchange Information ◽

Quantum Machine Learning ◽

Quantum Technology ◽

Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.

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Multiparticle quantum plasmonics

Nanophotonics ◽

10.1515/nanoph-2019-0517 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1243-1269 ◽

Cited By ~ 2

Author(s):

Chenglong You ◽

Apurv Chaitanya Nellikka ◽

Israel De Leon ◽

Omar S. Magaña-Loaiza

Keyword(s):

Quantum Dynamics ◽

Degrees Of Freedom ◽

Single Photon ◽

Quantum Systems ◽

Many Body ◽

Statistical Fluctuations ◽

Quantum Plasmonics ◽

Control Of Quantum Systems ◽

Multiparticle Systems ◽

Quantum Photonics

AbstractA single photon can be coupled to collective charge oscillations at the interfaces between metals and dielectrics forming a single surface plasmon. The electromagnetic near-fields induced by single surface plasmons offer new degrees of freedom to perform an exquisite control of complex quantum dynamics. Remarkably, the control of quantum systems represents one of the most significant challenges in the field of quantum photonics. Recently, there has been an enormous interest in using plasmonic systems to control multiphoton dynamics in complex photonic circuits. In this review, we discuss recent advances that unveil novel routes to control multiparticle quantum systems composed of multiple photons and plasmons. We describe important properties that characterize optical multiparticle systems such as their statistical quantum fluctuations and correlations. In this regard, we discuss the role that photon-plasmon interactions play in the manipulation of these fundamental properties for multiparticle systems. We also review recent works that show novel platforms to manipulate many-body light-matter interactions. In this spirit, the foundations that will allow nonexperts to understand new perspectives in multiparticle quantum plasmonics are described. First, we discuss the quantum statistical fluctuations of the electromagnetic field as well as the fundamentals of plasmonics and its quantum properties. This discussion is followed by a brief treatment of the dynamics that characterize complex multiparticle interactions. We apply these ideas to describe quantum interactions in photonic-plasmonic multiparticle quantum systems. We summarize the state-of-the-art in quantum devices that rely on plasmonic interactions. The review is concluded with our perspective on the future applications and challenges in this burgeoning field.

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