Quantum metasurface for multiphoton interference and state reconstruction

Wang, Kai; Titchener, James; Kruk, Sergey; Xu, Lei; Chung, Hung-Pin; Parry, Matthew; Kravchenko, Ivan I.; Chen, Yen-Hung; Solntsev, Alexander S.; Kivshar, Yuri S.; Neshev, Dragomir N.; Sukhorukov, Andrey A.

doi:10.1126/science.aat8196

Cited by 281 publications

(197 citation statements)

References 39 publications

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“…where { 3 | = {1, … ,9}} are real numbers. Equation (7) satisfies the condition defined by Eq. (3) and has the DOF of 9.…”

Section: Designing Non-paraxial Diffractive Optical Elements (Does)mentioning

confidence: 99%

“…By engineering the building blocks of the photonics materials and devices, the behavior of light, such as phase, amplitude and polarization of transmitted light and near field responses of wave, can be accurately controlled. Diverse photonic devices ranging from diffractive optical elements (DOE) to metamaterials (MM) and metasurfaces (MS) 1,2 , are designed for the extensive applications such as virtual/augmented reality display 3,4 , miniaturized imaging systems 5,6 , and quantum optics platform 7 . However, the complex mechanism of light-matter interaction prevents an intuitive strategy for the design of the building blocks in the photonic devices.…”

Section: Introductionmentioning

confidence: 99%

“…1 (d). We first construct the level set function of the original image ( , ) through the transform: 1 ( , ) = 345 (6,7) (1)…”

Section: Introductionmentioning

confidence: 99%

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Topological encoding method for data-driven photonics inverse design

Liu¹,

Zhu²,

Cai³

2020

Opt. Express

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Data-driven approaches have been proposed as effective strategies for the inverse design and optimization of photonic structures in recent years. In order to assist data-driven methods for the design of topology of photonic devices, we propose a topological encoding method that transforms photonic structures represented by binary images to a continuous sparse representation. This sparse representation can be utilized for dimensionality reduction and dataset generation, enabling effective analysis and optimization of photonic topologies with data-driven approaches. As a proof of principle, we leverage our encoding method for the design of two dimensional non-paraxial diffractive optical elements with various diffraction intensity distributions. We proved that our encoding method is able to assist machine-learning-based inverse design approach for accurate and global optimization.With the fast evolution of machine learning (ML) and deep learning (DL) techniques 12 , data-driven methods are emerging as an alternative way to discover and design photonic structures and devices 13 . Feedforward neural networks are leveraged for the approximation of the photonics systems with tens to hundreds of parameters, and have been utilized to successfully optimize photonic structures such as photonic crystals 14,15 , waveguide 16,17 , chiral metamaterials 18 , and metasurfaces 19 . When the degree of freedom (DOF) of the photonic system grows to thousands and more, convolutional neural networks (CNN) are adopted for the accurate prediction of the physical responses with much lower computational complexity 20,21 . Photonic structures represented in pixelated images, for example, are usually processed by CNNs to reduce the DOF for further optimization. Additionally, generative models, such as variational autoencoder (VAE) 22 and generative adversarial network (GAN) 23,24 , are utilized for the design of high DOF metasurface nanostructures in an expeditious way [25][26][27][28] . The stochastic nature of the generative models enables the exploration of the solution space in a global way. Consolidating with traditional optimization techniques, GAN and VAE are able to discover the topology of nanostructures with improved efficiency and robustness [29][30][31] .In the problems of inverse design of photonic structures, optimizing the topology of a photonic structure with arbitrary shape is a long-sought-after goal. Typically, the topology of photonic structures is represented in binary images. Because of the discretization and the high DOF of binary images, optimization is likely stuck in local minima. Although generative models are able to discover new topologies so as to approach global minimum, the bias of the training dataset and the limited capacity of the network cause incompleteness of the solution space, i.e., the global minimum may not be included in the space defined by the training dataset. Here, we propose an encoding method that is able to transform the binary image to a continuous sparse representation. This encoding appro...

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“…where { 3 | = {1, … ,9}} are real numbers. Equation (7) satisfies the condition defined by Eq. (3) and has the DOF of 9.…”

Section: Designing Non-paraxial Diffractive Optical Elements (Does)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Topological encoding method for data-driven photonics inverse design

Liu¹,

Zhu²,

Cai³

2020

Opt. Express

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show abstract

“…With the capability of direct engineering the optical response in both the near‐field and far‐field domain, metasurfaces has attracted great attention in manipulating the quantum state of light, [ 118 ] replacing the current bulky optical elements for the quantum measurements, such as lenses, beam splitters which has been commonly used in the quantum interference experimental measurements. In a recent work by Wang et al., [ 119 ] the researcher used the metasurface to realize and enable the single‐ and multiphoton state measurements based on the interaction of light with the subwavelength thin nanoresonators, as depicted in Figure a. It has been shown that using the silicon metasurface, it is able to reconstruct the multiphoton density matrices.…”

Section: Quantum Metasurface and Photon Sourcementioning

confidence: 99%

Trends in Quantum Nanophotonics

Huang

Woolley

et al. 2020

Adv Quantum Tech

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In this review, the nexus of nanophotonics and quantum science is considered. This nexus offers a wealth of applications and novel fundamental effects. Such an interdisciplinary approach may serve as a starting point for developing groundbreaking technologies. Several new directions in quantum nanophotonics are addressed, including strong light-matter coupling, bound states in the continuum, topological photonics, metasurfaces and photon sources, and levitated optomechanics. This review hopefully will foster the enhanced interaction between the nanophotonics and quantum science communities. Such a synergy will lead to the realization of novel communication, sensing, and information processing systems.

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“…Integrated robust broadband tunable Bell-state generators are of great potential for a wide range of applications in quantum photonics. Having access to a high quality photon-pair source will allow experimental tests of novel concepts, such as devices designed for quantum tomography [41,42], quantum state manipulation and storage [43], quantum state in-line reconstruction [44], quantum mechanics tests [45] and many others [46]. Tunability makes this source extremely versatile, while broadband operation gives the option to select the right output wavelengths for the task.…”

Section: Potential Applications In Quantum Photonicsmentioning

confidence: 99%

Broadband on-chip polarization mode splitters in lithium niobate integrated adiabatic couplers

et al. 2019

Self Cite

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We report, to the best of our knowledge, the first broadband polarization mode splitter (PMS) based on the adiabatic light passage mechanism in the lithium niobate (LiNbO 3 ) waveguide platform. A broad bandwidth of ~140 nm spanning telecom S, C, and L bands at polarization-extinction ratios (PER) of >20 dB and >18 dB for the TE and TM polarization modes, respectively, is found in a five-waveguide adiabatic coupler scheme whose structure is optimized by an adiabaticity engineering process in titanium-diffused LiNbO 3 waveguides. When the five-waveguide PMS is integrated with a three-waveguide "shortcut to adiabaticity" structure, we realize a broadband, high splitting-ratio (η c ) mode splitter for spatial separation of TE-(H-) polarized pump (700-850 nm for η c >99%), TM-(V-) polarized signal (1510-1630 nm for η c >97%), and TE-(H-) polarized idler (1480-1650 nm for η c >97%) modes. Such a unique integrated-optical device is of potential for facilitating the onchip implementation of a pump-filtered, broadband tunable entangled quantum-state generator.

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Quantum metasurface for multiphoton interference and state reconstruction

Cited by 281 publications

References 39 publications

Topological encoding method for data-driven photonics inverse design

Topological encoding method for data-driven photonics inverse design

Trends in Quantum Nanophotonics

Broadband on-chip polarization mode splitters in lithium niobate integrated adiabatic couplers

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