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The Interplay between Photonics and Machine Learning

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A special issue of Photonics (ISSN 2304-6732).

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 51568

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Special Issue Editors

Dr. Fulvio Flamini

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Guest Editor

Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020 Innsbruck, Austria
Interests: photonic quantum information; integrated quantum photonics; multi-photon interference; machine learning; artificial intelligence

Dr. Ilaria Gianani

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Guest Editor

Università degli Studi Roma Tre, Italy
Interests: quantum metrology, quantum optics, ultrafast optics, machine learning, quantum information

Prof. Dr. Fabio Sciarrino

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Guest Editor

Dipartimento di Fisica, Sapienza Università di Roma, Piazzale Aldo Moro 5, 00185 Rome, Italy
Interests: integrated quantum photonics; quantum metrology; photonics quantum information processing; foundations of quantum mechanics; quantum machine learning
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Dr. Valeria Cimini

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Guest Editor

Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy
Interests: experimental quantum optics; quantum information; quantum metrology

Special Issue Information

Dear Colleagues,

As is well known, the last two decades have seen a rapid surge of interest in photonics and machine learning. On one hand, optical technologies provide a well-established platform for countless applications in our everyday life, as well as in several areas of basic research. On the other hand, artificial intelligence and machine learning have established themselves as excellent tools for discovering, controlling, and interacting with complex systems. Within the scope of these two fields, the last decade has also seen an increase of interest in exploring where and how they can benefit from each other. More recently, these investigations have also been extended to quantum technologies, further enlarging the horizon of this broad line of research.

Motivated by the above achievements, it is our pleasure to announce a Special Issue that is entirely focused on their interplay. The intersection of these two fields is indeed drawing large attention, and its full potential is yet to be disclosed. Together, these results are paving the way for broader and deeper investigations, which we aim to collect here.

This Special Issue is dedicated to theoretical or experimental advances bringing together the fields of classical/quantum optical technologies and classical/quantum machine learning. Relevant areas of interest include but are not limited to the following topics:

Supervised and unsupervised learning for optical applications;
Reinforcement learning algorithms to control optical systems;
Design and implementation of intelligent systems using optical technologies;
Design and implementation of energy-efficient optical platforms for machine learning;
Machine learning for characterizing and optimizing quantum states of light;
Quantum machine learning with quantum optics systems.

Dr. Fulvio Flamini
Dr. Ilaria Gianani
Prof. Dr. Fabio Sciarrino
Dr. Valeria Cimini
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Photonics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (11 papers)

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Research

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14 pages, 5818 KiB

Open AccessArticle

Electro-Optical Sensors for Atmospheric Turbulence Strength Characterization with Embedded Edge AI Processing of Scintillation Patterns

by Ernst Polnau, Don L. N. Hettiarachchi and Mikhail A. Vorontsov

Photonics 2022, 9(11), 789; https://doi.org/10.3390/photonics9110789 - 24 Oct 2022

Cited by 3 | Viewed by 2263

Abstract

This study introduces electro-optical (EO) sensors (TurbNet sensors) that utilize a remote laser beacon (either coherent or incoherent) and an optical receiver with CCD camera and embedded edge AI computer (Jetson Xavier Nx) for in situ evaluation of the path-averaged atmospheric turbulence refractive [...] Read more.

C_{n}^{2}

at a high temporal rate. Evaluation of

C_{n}^{2}

values was performed using deep neural network (DNN)-based real-time processing of short-exposure laser-beacon light intensity scintillation patterns (images) captured by a TurbNet sensor optical receiver. Several pre-trained DNN models were loaded onto the AI computer and used for TurbNet sensor performance evaluation in a set of atmospheric propagation inference trials under diverse turbulence and meteorological conditions. DNN model training, validation, and testing were performed using datasets comprised of a large number of instances of scintillation frames and corresponding reference (“true”)

C_{n}^{2}

values that were measured side-by-side with a commercial scintillometer (BLS 2000). Generation of datasets and inference trials was performed at the University of Dayton’s (UD) 7-km atmospheric propagation test range. The results demonstrated a 70–90% correlation between

C_{n}^{2}

values obtained with the TurbNet sensors and those measured side-by-side with the scintillometer. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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16 pages, 4081 KiB

Open AccessArticle

ReDDLE-Net: Reflectance Decomposition for Directional Light Estimation

by Jiangxin Yang, Binjie Ding, Zewei He, Gang Pan, Yanpeng Cao, Yanlong Cao and Qian Zheng

Photonics 2022, 9(9), 656; https://doi.org/10.3390/photonics9090656 - 15 Sep 2022

Cited by 2 | Viewed by 1709

Abstract

The surfaces of real objects can visually appear to be glossy, matte, or anywhere in between, but essentially, they display varying degrees of diffuse and specular reflectance. Diffuse and specular reflectance provides different clues for light estimation. However, few methods simultaneously consider the contributions of diffuse and specular reflectance for light estimation. To this end, we propose ReDDLE-Net, which performs Reflectance Decomposition for Directional Light Estimation. The primary idea is to take advantage of diffuse and specular clues and adaptively balance the contributions of estimated diffuse and specular components for light estimation. Our method achieves a superior performance advantage over state-of-the-art directional light estimation methods on the DiLiGenT benchmark. Meanwhile, the proposed ReDDLE-Net can be combined with existing calibrated photometric stereo methods to handle uncalibrated photometric stereo tasks and achieve state-of-the-art performance. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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13 pages, 10184 KiB

Open AccessArticle

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

by Ehsan Eftekhari-Zadeh, Abdallah S. Bensalama, Gholam Hossein Roshani, Ahmed S. Salama, Christian Spielmann and Abdullah M. Iliyasu

Photonics 2022, 9(6), 382; https://doi.org/10.3390/photonics9060382 - 26 May 2022

Cited by 6 | Viewed by 2394

Abstract

Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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11 pages, 11707 KiB

Open AccessArticle

Dual-Branch Feature Fusion Network for Salient Object Detection

by Zhehan Song, Zhihai Xu, Jing Wang, Huajun Feng and Qi Li

Photonics 2022, 9(1), 44; https://doi.org/10.3390/photonics9010044 - 14 Jan 2022

Viewed by 2533

Abstract

Proper features matter for salient object detection. Existing methods mainly focus on designing a sophisticated structure to incorporate multi-level features and filter out cluttered features. We present the dual-branch feature fusion network (DBFFNet), a simple effective framework mainly composed of three modules: global information perception module, local information concatenation module and refinement fusion module. The local information of a salient object is extracted from the local information concatenation module. The global information perception module exploits the U-Net structure to transmit the global information layer by layer. By employing the refinement fusion module, our approach is able to refine features from two branches and detect salient objects with final details without any post-processing. Experiments on standard benchmarks demonstrate that our method outperforms almost all of the state-of-the-art methods in terms of accuracy, and achieves the best performance in terms of speed under fair settings. Moreover, we design a wide-field optical system and combine with DBFFNet to achieve salient object detection with large field of view. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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11 pages, 844 KiB

Open AccessCommunication

Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics

by Shu-Hao Chang

Photonics 2022, 9(1), 33; https://doi.org/10.3390/photonics9010033 - 7 Jan 2022

Cited by 1 | Viewed by 2759

Abstract

Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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9 pages, 379 KiB

Open AccessArticle

Quantum Optical Experiments Modeled by Long Short-Term Memory

by Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler and Sepp Hochreiter

Photonics 2021, 8(12), 535; https://doi.org/10.3390/photonics8120535 - 26 Nov 2021

Cited by 7 | Viewed by 4338

Abstract

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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10 pages, 6061 KiB

Open AccessArticle

Implementation of Pruned Backpropagation Neural Network Based on Photonic Integrated Circuits

by Qi Zhang, Zhuangzhuang Xing and Duan Huang

Photonics 2021, 8(9), 363; https://doi.org/10.3390/photonics8090363 - 30 Aug 2021

Cited by 3 | Viewed by 3827

Abstract

We demonstrate a pruned high-speed and energy-efficient optical backpropagation (BP) neural network. The micro-ring resonator (MRR) banks, as the core of the weight matrix operation, are used for large-scale weighted summation. We find that tuning a pruned MRR weight banks model gives an equivalent performance in training with the model of random initialization. Results show that the overall accuracy of the optical neural network on the MNIST dataset is 93.49% after pruning six-layer MRR weight banks on the condition of low insertion loss. This work is scalable to much more complex networks, such as convolutional neural networks and recurrent neural networks, and provides a potential guide for truly large-scale optical neural networks. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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Review

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27 pages, 1855 KiB

Open AccessReview

Leveraging AI in Photonics and Beyond

by Gandhi Alagappan, Jun Rong Ong, Zaifeng Yang, Thomas Yong Long Ang, Weijiang Zhao, Yang Jiang, Wenzu Zhang and Ching Eng Png

Photonics 2022, 9(2), 75; https://doi.org/10.3390/photonics9020075 - 28 Jan 2022

Cited by 12 | Viewed by 11082

Abstract

Artificial intelligence (AI) techniques have been spreading in most scientific areas and have become a heated focus in photonics research in recent years. Forward modeling and inverse design using AI can achieve high efficiency and accuracy for photonics components. With AI-assisted electronic circuit design for photonics components, more advanced photonics applications have emerged. Photonics benefit a great deal from AI, and AI, in turn, benefits from photonics by carrying out AI algorithms, such as complicated deep neural networks using photonics components that use photons rather than electrons. Beyond the photonics domain, other related research areas or topics governed by Maxwell’s equations share remarkable similarities in using the help of AI. The studies in computational electromagnetics, the design of microwave devices, as well as their various applications greatly benefit from AI. This article reviews leveraging AI in photonics modeling, simulation, and inverse design; leveraging photonics computing for implementing AI algorithms; and leveraging AI beyond photonics topics, such as microwaves and quantum-related topics. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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38 pages, 1714 KiB

Open AccessReview

Machine Learning Applications for Short Reach Optical Communication

by Yapeng Xie, Yitong Wang, Sithamparanathan Kandeepan and Ke Wang

Photonics 2022, 9(1), 30; https://doi.org/10.3390/photonics9010030 - 4 Jan 2022

Cited by 29 | Viewed by 8156

Abstract

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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12 pages, 1931 KiB

Open AccessReview

Secure Continuous-Variable Quantum Key Distribution with Machine Learning

by Duan Huang, Susu Liu and Ling Zhang

Photonics 2021, 8(11), 511; https://doi.org/10.3390/photonics8110511 - 13 Nov 2021

Cited by 11 | Viewed by 3026

Abstract

Quantum key distribution (QKD) offers information-theoretical security, while real systems are thought not to promise practical security effectively. In the practical continuous-variable (CV) QKD system, the deviations between realistic devices and idealized models might introduce vulnerabilities for eavesdroppers and stressors for two parties. However, the common quantum hacking strategies and countermeasures inevitably increase the complexity of practical CV systems. Machine-learning techniques are utilized to explore how to perceive practical imperfections. Here, we review recent works on secure CVQKD systems with machine learning, where the methods for detections and attacks were studied. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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8 pages, 304 KiB

Open AccessReview

Quantum Reinforcement Learning with Quantum Photonics

by Lucas Lamata

Photonics 2021, 8(2), 33; https://doi.org/10.3390/photonics8020033 - 28 Jan 2021

Cited by 14 | Viewed by 6077

Abstract

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. Full article

(This article belongs to the Special Issue The Interplay between Photonics and Machine Learning)

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