Deep Learning for Photonic Design and Analysis: Principles and Applications

Duan, Bing; Wu, Bei; Chen, Jinhui; Chen, Huanyang; Yang, Daquan

doi:10.3389/fmats.2021.791296

Cited by 17 publications

(5 citation statements)

References 82 publications

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“…These discussions advocate for the utilization of quasi-biological constructs [43], nanotechnology-driven architectures [44], and related paradigms. Given the intimate interplay between cuttingedge computational systems and neural networks (pertinently exemplified by quantum computing systems [45,46] and optical neural networks [47,48]), an exploration of neural network attributes grounded in hydrophilic polymers emerges as an avenue of applied interest within this context.…”

Section: Introductionmentioning

confidence: 99%

Formation of Hydrophobic–Hydrophilic Associates in the N-Vinylpyrrolidone and Vinyl Propyl Ether Copolymer Aqueous Solutions

Kabdushev,

Mun,

Suleimenov

et al. 2023

Polymers

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Utilizing turbidimetry data, an examination is conducted on the behavior of solutions containing N-vinylpyrrolidone and vinyl propyl ether copolymer within a temperature range coinciding with the occurrence of a phase transition. The investigation reveals that within specific conditions prevailing in this domain, the emergence of entities denoted as hydrophobic–hydrophilic associates is conceivable. These entities are characterized by the presence of a relatively dense core, upheld by hydrophobic interplays, and they are proficient in effectively dispersing irradiation within the optical spectrum. Encircling this core is a hydrophilic periphery that impedes the formation of insoluble precipitates. The development of such associates transpires when hydrophobic interactions have attained a discernible prominence, although they remain inadequate to counteract the forces that drive the expansion of macromolecular coils. Under these circumstances, the energetically favored course of action entails the constitution of a core for the aforementioned associates, involving discrete segments from diverse macromolecules. Notably, the introduction of an additional constituent (ethanol) to the solution, which selectively mitigates hydrophobic interactions, serves to stabilize the hydrophobic–hydrophilic associations.

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Section: Introductionmentioning

confidence: 99%

Formation of Hydrophobic–Hydrophilic Associates in the N-Vinylpyrrolidone and Vinyl Propyl Ether Copolymer Aqueous Solutions

Kabdushev,

Mun,

Suleimenov

et al. 2023

Polymers

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“…Reviews on the recent advances of machine learning, in particular deep learning, for the photonic structure design and optical data analysis, as well as the challenges and perspectives, can be found in Refs. [48,[367][368][369]. Inverse designs and optimization on the photonic crystals, plasmonic nano-structures, programmable meta-materials, and meta-surfaces have been actively explored for high-speed optical communication and computing, ultrasensitive biochemical detection, efficient solar energy harvesting, and super-resolution imaging [370].…”

Section: Photonic Quantum Computingmentioning

confidence: 99%

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…We applied a deep learning (DL) algorithm to process the lens data in the wide-angle fisheye lens design. Deep learning is a subfield of machine learning based on artificial neural networks [24][25][26][27]. The first challenge of applying DL to optical design is that a deep neural network (DNN) model can be trained to predict the response of a given design, but the opposite is not possible.…”

Section: Deep Learning Algorithmmentioning

confidence: 99%

Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques

2022

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This paper presents the optimization design of a miniaturized five-element wide-angle fisheye lens using a deep learning algorithm. Zemax optical design software was used to simulate and optimize the wide-angle fisheye lens. A deep learning algorithm helped to find the best combination of different lens materials. We first used six lens elements as an initial configuration to design miniaturized wide-angle fisheye lenses using the optimization process. The optical system components were gradually decreased to five lens elements. Both OKP4HT and polymethyl methacrylate (PMMA) plastic aspheric lenses were selected to replace the second spherical glass lens in the original design. We propose two types of wide-angle fisheye lens designs with four spherical lenses and one aspheric lens. The results for these designs indicated a viewing angle of 174°, a total length of less than 15 mm, a spot size of less than 6 μm, lateral color within ±1 μm, field curvature within ±0.02 mm, and F-θ distortion of ±3.5%. In addition, the MTF value was larger than 0.4 at the spatial frequency of 100 cycles/mm.

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Deep Learning for Photonic Design and Analysis: Principles and Applications

Cited by 17 publications

References 82 publications

Formation of Hydrophobic–Hydrophilic Associates in the N-Vinylpyrrolidone and Vinyl Propyl Ether Copolymer Aqueous Solutions

Formation of Hydrophobic–Hydrophilic Associates in the N-Vinylpyrrolidone and Vinyl Propyl Ether Copolymer Aqueous Solutions

Quantum Machine Learning: from physics to software engineering

Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques

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