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Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processing quantum information. Here, we propose and experimentally demonstrate a programmable metasurface capable of performing quantum algorithms using both classical and quantum light with single photons. Our approach encodes multiple programmable quantum algorithms and operations, such as Grover's search algorithm and the quantum Fourier transform, onto the same metalens array on a metasurface. A spatial light modulator selectively excites different sets of metalenses to carry out the quantum algorithms, while the interference patterns captured by a single-photon camera are used to extract information about the output state at the selected output directions. Our programmable quantum metasurface approach holds promising potential as a cost-effective means of miniaturizing components for quantum computing and information processing.

We demonstrate an integrated deep-learning neural network that generates metasurface designs directly from independent polarization holograms, with a maximum of four different co- and cross- polarization conversion channels. This is achieved by extending the existing DeepCGH network with an inverse designcomponent. Our approach enables a systematic design route for polarization holograms directly from an existing metamaterial library without requiring detailed knowledge of the constraints. Furthermore, it can be adapted for other multiplexing holograms that require automated designs. © 2023, META Conference. All rights reserved.

We propose and experimentally demonstrate a programmable matrix computation in the optical domain using a spatial light modulator (SLM) and a metalens array. Our scheme encodes the programmable matrix to the metalens array by superimposing multiple phase gradients. Meanwhile, the SLM produces a phase-controlled spatial illuminated light which acts as our phase-controlled spatial illuminated light which acts as our input vector. Then, the result can be extracted from the far-field interference pattern constructed by different metalenses using a pattern recognition method. © 2023, META Conference. All rights reserved.

Multiplexing holography with metasurfaces using different degrees of freedom of light has enabled recent applications in display and information processing. In terms of polarization-multiplexed holograms, the most general form is an arbitrary Jones matrix profile in storing the maximum amount of information. It requires a relaxation to bianisotropic metasurfaces from a conventional single-layer implementation of nanostructures, but it will also complicate both the inverse design of the nanostructures and the hologram generation algorithm. Here, an integrated neural network approach, being extended from the recent DeepCGH algorithm, is developed to obtain metasurface structural profiles directly from independent holograms from an arbitrary set of polarizations to another, with maximally four different co- and cross-polarization conversion channels. Such an information-driven approach enables designing complex polarization holograms directly from an existing metamaterial library without detailed physical knowledge on the constraints, and can be extended to other multiplexing holograms to further facilitate an efficient usage of the information stored on a metasurface. © 2023 The Authors. Advanced Optical Materials published by Wiley-VCH GmbH.

Two-photon interference is an important effect that is tightly related to the quantum nature of light. Recently, it has been shown that the photon bunching from the Hong-Ou-Mandel (HOM) effect can be used for quantum imaging in which sample properties (reflection/transmission amplitude, phase delay, or polarization) can be characterized at the pixel-by-pixel level. In this work, we perform Jones matrix imaging for an unknown object based on two-photon interference. By using a reference metasurface with panels of known polarization responses in pairwise coincidence measurements, the object's polarization responses at each pixel can be retrieved from the dependence of the coincidence visibility as a function of the reference polarization. The post-selection of coincidence images with specific reference polarization in our approach eliminates the need in switching the incident polarization and thus parallelized optical measurements for Jones matrix characterization. The parallelization in preparing input states, prevalent in any quantum algorithms, is an advantage of adopting two-photon interference in Jones matrix imaging. We believe our work points to the usage of metasurfaces in biological and medical imaging in the quantum optical regime.

Metasurfaces provide a promising route for structuring light and generating holograms with designed amplitude, phase, and polarization profiles, leading to a versatile platform for integrating and constructing optical components beyond the conventional ones. At the same time, incorporating coincidence in imaging allows a high signal-to-noise ratio for imaging in very low light levels. As beneficial from the recent development in both metasurfaces and single-photon avalanche diode (SPAD) cameras, we combine the polarization-sensitive capability of metasurfaces with Hong-Ou-Mandel (HOM)-type interference in generating images with tailor-made two-photon interference and polarization coincidence signatures. By using orthogonal linear-polarized photons as incidence, correlated, anticorrelated, and uncorrelated polarization coincidence features can be observed within the same image from the pairwise second-order coherence statistics across different pixels of the image. Our work adds polarization to the demonstrated amplitude and phase sensitivity in the domain of “HOM microscopy” and can be useful for biological and security applications.

We demonstrate extraction of spatially dependent material parameters by using unsupervised neural network in learning the data structure of the wave propagating data from a given wave equation. A 2D spring mass model is used to image mass or modulus distribution, as a simplified model for acoustic imaging. The approach facilitates the discovery of spatially dependent differential equation coefficients and can be applied to different waves, without prior knowledge of scattering mechanism and is applicable to inverse scattering with metamaterials. © 2023, META Conference. All rights reserved.

We approach the inverse problem of imaging as a task of automated feature learning of the underlying wave equation. While feature extraction with unsupervised machine learning is widely used in analyzing complex data on clustering, classification, and visualization, we show its direct usage on discovering interpretable physical concepts and then obtaining images from wave-propagation data. By using a spring-mass lattice as a simplified model in acoustic imaging, a variational autoencoder is trained to extract features that govern the dynamics of wave propagation from configurations with uncorrelated random material parameters. The extracted features are transformed to images of spring constants and masses by an additional linear regression. The current scheme of extraction-based inverse imaging of features is robust against noise in wave-propagation data and incomplete accessibility in the case of inverse scattering in practice. Our approach requires minimal prior knowledge of the wave-scattering mechanism with applications from the extraction of physical constants and defect detection to discovering physical phenomena.