Reviewerly/exHarmony huggingface dataset:
Single-Mode Quantum Non-Gaussian Light from Warm Atoms

Quantum non-Gaussianity (QNG) corresponds to an essential resource in many prospective applications of quantum technologies. QNG states of light have been demonstrated in a broad range of experimental platforms with various complementary advantages [1]. However, in order to allow for their direct and efficient utilization for interaction with the target quantum system, their degrees of freedom have to be well-defined and controllable. This imposes a requirement on the generation of QNG light in a single optical mode. While the intrinsic single-photon sources including trapped ions, atoms, or molecules, naturally offer a close-to a single mode emission, their technologically more accessible counterparts including majority of the solid-state emitters and sources based on heralded generation of nonclassical states in various parametric nonlinear processes typically provide a multi-mode output. Although several demonstrations of a single-mode operation of nonclassical light sources based on these platforms have been recently demonstrated, an unambiguous observation of a single-mode QNG light has never been presented.
Enhancing Ising Machine Robustness Through Period-2 Dynamics in Optical Resonators

Here we propose a novel coherent Ising machine (CIM) based upon spontaneous symmetry-breaking (SSB) in a driven Kerr resonator [Fig. 1a]. Specifically, we consider a ring resonator made of a Kerr nonlinear waveguide (e.g. optical fibre) that is coherently driven with a train of short pulses with a repetition rate an integer multiple of the cavity free-spectral range. For appropriate parameters, each of the pulses undergoes spontaneous polarization symmetry breaking [SSB] within the resonator, thus yielding independent spin-like states. By measuring the polarization states of each of the pulses and feeding the measurement results to a phase modulator that acts upon the driving field, we are able to obtain a network of spins that are coupled according in a desired manner. Scanning a control parameter (e.g. laser wavelength) across the SSB bifurcation point when the coupling is activated forces the spins to evolve towards the ground-state of the corresponding Ising problem. By operating in a recently-discovered period-2 (P2) regime, where the polarization modes of the resonator switch at each roundtrip due to the implementation of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pi$</tex> -phase shift between two orthogonal polarisation modes of the resonator, the SSB dynamics are completely protected against asymmetries [1]. This includes both asymmetries in the feedback mechanism and the driving laser polarization state.
A High-Fidelity Reconfigurable Photonic Processor for NISQ Computing

Photons are excellent carriers of quantum information: They are resistant to decoherence and can easily propagate over large distances. Mature technologies for controlling their creation, propagation, and detection are now available. This, along with their capabilities for interfacing between other quantum platforms give them a unique standing in the development of applications in the NISQ era and beyond.
An Autonomous Semiconductor Laser Neural Network

Artificial Neural Networks (ANNs) have become a ubiquitous technology; indeed, their flexibility allows them to excel in a wide range of tasks, ranging from medical diagnosis to language models. Contrary to classical algorithms, these networks process information in parallel. Photonics, in particular, shows great promise as a platform for implementing ANNs in terms of scalability, speed, energy efficiency and parallel information processing [1]. In [2], we physically implemented the first fully autonomous PNN (photonic neural network), using spatially multiplexed modes of an injection locked large area vertical cavity surface emitting laser (LA-VCSEL). All components of our PNN, including learning are fully realized in hardware using off-the-shelf, commercially available, low energy consumption components, while still achieving >98% accuracy in 6-bit header recognition tasks.
Localizing Eavesdroppers Inside a Quantum Channel Using Stimulated Brillouin Scattering

While modern communication systems pose a high standard of security using state-of-the-art encryption techniques based on computational hardness problems, quantum key distribution (QKD) offers a unique advantage using the laws of quantum mechanics. A secret key is shared between two parties, using quantum states of light over a quantum channel. Due to the quantum nature of the states, any interception or other way of eavesdropping will manifest in the distortion of the quantum state resulting in a change of accessible parameters. Whenever such a change of parameters is noticed, the shared key is marked unsecure and hashed down largely or, in the worst case, discarded.
Photonic Tensor Core &amp; Convolution Chip for Machine Learning Acceleration

Here we present our architecture for Silicon Photonic Tensor Core (PTC) capable of accelerating computational needs of neural networks and augmented/virtual reality applications. We present a novel fully-integrated PTC including chip-based lasers, modulators, and photodetectors. © 2022 The Author(s)
Influence of Absorber Carrier Lifetimes on the Excitability Regime of an Integrated Two-Section InP Laser Neuron

Inspired by the low energy consumption of the human brain, neuromorphic engineering aims to develop novel hardware to aid the development of machine learning algorithms [1]. Integrated photonics is regarded as a formidable candidate to develop a low-power, ultra-fast optical neural network, due to the high bandwidth, low crosstalk [2] and temporal characteristics governed by fast carrier dynamics. In an all-optical photonic spiking neural network (SNN), a laser neuron is triggered by an optical pulse of sufficient energy generated by previous neurons. From a dynamics perspective this thresholding functionality is an excitable response to an input perturbation and can be realized through integrated two-section lasers [31].
Fully On-Chip Laser-Integrated Quantum Source of Entangled Photon States

Integrated quantum photonics can realize and process optical entangled quantum states in highly compact, robust, and scalable chips thereby enabling chip scale implementations of long-distance quantum-secured communication, quantum-accelerated information processing, and non-classical metrology [1]. Notably, all previous on-chip entangled quantum photonic sources have relied on an external laser to excite nonlinear parametric processes, thereby making these systems overall non-reproducible, bulky, impractical, and thus unsuitable for out-of-lab use as well as production at large scale [2]. To date, the major challenge inhibiting a fully on-chip quantum light system is to integrate a stable, tunable laser together with a high rejection filter that eliminates laser sideband noise [3], [4] and a nonlinear parametric source of entangled photons. Fig. 1 A) laser-integrated photonic quantum light source of frequency-bin entangled photon states: an electrically pumped inp gain-section, i.e. A reflective semiconductor optical amplifier (rsoa) providing optical gain, is coupled to a si3n4 chip containing a three microring resonator (r1, r2, and r3) vernier filter and a mach-zehnder interferometer (mzi) with a sagnac loop mirror. B) measurements utilizing single photon detectors allowed to determine an smsr≈ 112 db, and an ase suppression of> 56 db. C) car for respective frequency pairs for an excitation power of 3.9 mw. D) density matrices of two-dimensional (qubit) and three-dimensional (qutrit) frequency-bin entangled states reconstructed via quantum state tomography (qst).
Quantum Optics Tensor Networks in Time for the Design of Nonlinear Photonic Devices

Matrix product states (MPS) provide a memory efficient way to store high dimensional many body quantum systems [1]. Each degree of freedom is assigned a tensor and connecting each are bond matrices which capture correlations between different degrees of freedom as depicted in Fig 1. (a). Next efficient time evolution is then achieved by time evolving block decimation (TEBD) [2]. The essence of TEBD is that given a local Hamiltonian and a small time step the resulting unitary transformation can be decomposed into a collection of commuting two-site and single site gates acting on our MPS as shown in Fig 1. (b). With this formalism we care able to describe 1D waveguide systems with dispersion profiles up to arbitrary order and evolve states under a Hamiltonian with a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\chi^{(3)}$</tex> non linearity [3]. Linear loss and two photon absorption can be included in the model through the materials imaginary dispersion data. Arbitrary spatial, temporal and spectral pump profiles may also be included in the driving of parametric processes. Given an initial quantum field and classical drives we can simulate the temporal dynamics of the quantum field. From this we extract the familiar measurable quantities including spectral content, temporal profile, correlators or any well defined observable of the field. In Fig 2. (c) We show the spectrum of a weak coherent state at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2050\text{nm}\ (\vert\alpha\vert =1)\ (\text{red})$</tex> , which undergoes stimulated 4 wave mixing via a classical pump at 2070nm (purple). After the interaction the resulting spectra (blue) contains stimulated photon generation at the phase matched idler wavelength of 2090nm and parametric amplification of the stimulating field. Fig 2. (d) shows an example of the field expectation value and variance for the underlying quantum state before (red) and after (blue) the interaction.
High-resolution consistency analysis for performance evaluation of photonic time-delay reservoir computers

Time-Delayed Reservoir computing (TDRC) is an approach to implement artificial neural using a single physical dynamical system or node with a delayed feedback loop. Various photonic implementations have been proposed, most notably those using semiconductor lasers with coherent optical feedback [1], [2]. One of the metrics used to assess the performance of reservoir computing is the dynamical consistency [3], which evaluates the system's capacity to generate similar outputs with different initial conditions and when driven with similar input signals.
Certification of Non-Gaussian States using Double Homodyne Detection

Certifying reliable sources for quantum information processing is still one of the major challenges towards practical applications. Continuous variable quantum information provides a very promising platform where important resources such as scalable entanglement come “for free” [1]. In that context, a crucial quantum feature to look for and investigate is the so-called non-Gaussianity, which, beyond its conceptual relevance, is a necessary resource for any quantum computational advantage as well as for effective error correction, entanglement distillation and more. Non-Gaussian states have been produced and probed using full tomography techniques [2]. However, performing a full tomography of multimode continuous variable systems is a hard problem. Indeed, systems aimed at overcoming classical approach are by definition too complex to be fully characterized, thus alter-native approaches are required. In this work, we report on the use of double homodyne detection (see on Fig. 1(a)) on optical frequency comb pulses for the certification of non-Gaussian features of quantum states.
Neuromorphic Camera Assisted High-Flow Imaging Cytometry for Particle Classification

In this work we combine a high flow cytometry experimental setup and a 10Kframe/sec capable neuromorphic event-based camera, followed by lightweight machine learning schemes, thus allowing the simultaneous imaging and real-time classification of test particles, moving at a speed of 0.8m/sec with an accuracy of 97.6%. The key advantage of the utilized microscopy system, is the use of an event-based camera, generating spiking events, triggered by pixel's contrast changes. This bio-inspired operation, contrary to conventional CMOS cameras [1], alleviates bandwidth constraints and can significantly boost frame-rate capabilities, thus capturing high speed events. Following this paradigm, medical imaging modalities, where the detection and analysis of fast-moving particles is a necessity, such as high-flow cytometry, can greatly proliferate from the proposed approach.
Multi-Channel Time-Bin Quantum Key Distribution over a 70-km-Long Deployed Fiber Link

Quantum key distribution (QKD), in which security is ensured by physical quantum laws, is shown as the most secure communication between two distant users [1]–[3]. Recent efforts in global research focus on providing the infrastructure that can support QKD and extending applications to large scale of users and distances. Three types of links are explored up to date: terrestrial free-space links (limited by visible distance), fiber-based links (repeater-free range limited to a few 100 km [1]) and finally satellite links, which allow to bridge intercontinental distances. For each of these scenarios, different types of hardware are involved and state encoding (polarization-, time- [1]–[2], spatial- and frequency-based [3]) is tailored to mitigate link impairments. Additionally, the effort towards higher QKD rates is given [1]–[3] that future global quantum communication networks support a wider range of use cases.
Quantum Enhanced Integrated Single Photon Nonlinear Interferometer

The standard quantum limit (SQL) describes the best precision that can be achieved for phase measurements using a classical interferometer. For such an interferometer, this limit is given by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/\sqrt{N}$</tex> where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> is the total number of photons launched into the interferometer. The SQL can be circumvented through the utilisation of quantum states combined with advanced low-loss detection schemes. Such overcoming of the SQL has been demonstrated, for example, by replacing the unused vacuum input port of the linear interferometer with a non-classical state [1], and by exploiting nonlinear SU (1,1) interferometers in the high-gain regime [2]. However, the impact of the experimental losses on the ability of nonlinear interferometers to beat the SQL is often neglected, particularly in the low-gain regime. In this work, we close this gap by studying the performance of lossy SU (1,1) interferometers in the single-photon pair regime, with particular attention to the different amount of information contained in the measurement of single counts and of coincidences at the output of the interferometer.
A Simulation Framework for Feedforward in Quantum Photonic Systems

It has been known for over two decades that measurement and feedforward—whereby a optical network is dynamically adjusted conditional on single photon measurements—is the only viable path to building scalable quantum technology with photons [1]. Despite this, there has been little work done to simulate these systems physically. Previous models typically are classical, for example stabliser-based approaches in error-correction protocols [2], or operate entirely in the frequency domain. These frequency domain methods provide a simple method to produce an output distribution from a system unitary and its input state [3], but necessarily omit the temporal dynamics of the system, and hence are difficult to apply to systems with measurement and feedforward.
Visualizing and Understanding Optoelectronic Neural Networks via the Orbital Angular Momentum of Light

As deep learning is penetrating almost every field of research and industry, the demand for computational improvements drives us to investigate new computing paradigms, such as optical and hybrid optoelectronic neural networks. Despite continuous efforts in both free-space and on-chip pathways, scant attention was paid to algorithms suitable for these new hardware. Here we introduce a model visualization method for diffractive optoelectronic neural network (see Fig. 1(a)), proposed in [1]. Specifically, we implement the visualization in a scenario of measuring orbital angular spectrum (OAM) of vortex light, a challenging task in OAM community. Our findings may inspire the link between intelligent computing and physical effects and applications.
A Quantum-Bit Encoding Converter

To advance quantum information technologies, many quantum devices and protocols are being developed based on a variety of physical systems, including photons, atoms, mechanical oscillators, solidstate or superconducting devices. Implementations do not only choose between the vast landscape of platforms but also between favoured degrees of freedom and the advantages they can provide [1]. The capability to convert between different encodings is key to linking separate devices in a future heterogeneous quantum network [2]. Such a process is challenging as it requires one to preserve fragile quantum superpositions during the transcoding while functioning as a black box with propagating input and output qubits, a requisite that demands the process to be heralded and free of post-selection. Here we report the experimental realization of such a converter in the optical domain [3], [4], schematically shown in Fig. 1a.
Lithium Niobate on Insulator Photonic Networks as an Integrated Multiple Scattering Platform

Multiple scattering in complex random media is a resource for computation, in the form of reservoir computing [1] as well as boson sampling and random walks [2]. In this work, we develop lithium niobate on insulator (LNOI) waveguide networks as an on-chip platform for multiple scattering. This approach combines the computational potential of random scattering with the control of top-down fabrication, with the addition of the electro-optic property of LNOI allowing for further tuning capabilities [3], resulting in integrated multiple scattering devices with designed randomness and controllable scattering parameters.
Non-Adiabatic Holonomic Quantum Gates

Implementing quantum gates as non-Abelian holonomies, a class of topologically protected unitary operators, is a particularly promising paradigm for the design of intrinsically stable quantum computers [1]. In contrast to dynamic phases, the geometric phase accumulated by a quantum system propagating through a Hilbert space <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{H}$</tex> depends exclusively on its path. In general, geometric phases can exhibit arbitrary dimensionality. Wilczek and Zee introduced the idea of multi-dimensional, non-Abelian geometric phases - so called holonomies [2]. Anandan later dropped the requirement of adiabaticity to create holonomies, that are truly time-independent [3]. Non-adiabatic holonomies rely on a subspace <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{H}_{\text{geo}}$</tex> of the Hilbert-space that is spanned by states <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{\vert \Phi_{k}\rangle\}_{k}$</tex> that fulfill <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\Phi_{k}\vert \hat{H}\vert \Phi_{j}\rangle=0$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{H}$</tex> is the system's Hamiltonian. Restricting the propagation to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{H}_{\text{geo}}$</tex> ensures parallel transport and, thus, a purely geometric phase (see Fig. 1a) [4], [5]. Quantum optics constitutes a particularly versatile platform for quantum information processing, and in particular for the construction of non-adiabatic holonomic quantum computers: In addition to integration and miniaturization provided by the platform, the bosonic nature of photons also conveniently allows for multiple excitations of the same mode, readily expanding <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{H}_{\text{geo}}$</tex> and enabling the synthesis of holonomies from higher symmetry groups <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{U}(N)$</tex> as larger and more capable computational units [6], [7].
From passions to emojis

Abstract This chapter discusses the history of verbal and visual languages of emotion. It highlights how words are powerful witnesses and tools for social and emotional change. The concepts such as ‘emotives’ and ‘emotional communities’ help historians identify the social work that emotion language is doing. Additionally, emotional states can be conveyed through non-verbal language, including both facial expression and bodily gestures. Emoticons and emojis are the most recent additions to the array of emotional languages. Evolving originally from playful uses of typographical features to represent facial expressions of feeling, these symbols are now ubiquitous on smartphones and social media.
A Novel Hybrid CNN-LSTM Approach for Handwritten Text Recognition for the Washington Database

The handwritten text recognition (HTR) is a challenging task in the field of pattern recognition and machine learning. In this paper, a novel hybrid approach based on CNN and LSTM networks is proposed for HTR on the Washington database. The proposed approach consists of two stages. In the first stage, a CNN is used to extract features from the input images. The output of the CNN is then fed into a bidirectional LSTM network in the second stage for recognizing the handwritten text. The proposed approach was evaluated on the Washington database, which contains a large number of handwritten documents with various writing styles and languages. The experimental results showed that the proposed approach outperforms in terms of Training loss, validation loss and testing loss. Overall, the proposed hybrid CNN-LSTM approach offers a promising solution for HTR tasks on complex and diverse datasets like the Washington database.
Comparison between Machine Learning Classifier Based On Face Recognition

With face recognition, machine learning is one of the computer sciences fields that is getting bigger the quickest. The goal of this study is to give a basic overview of machine learning and the algorithmic paradigms it provides. The study gives a detailed explanation of the basic ideas behind machine learning and the math that turns these ideas into methodologies that can be used in the real world, and discusses and compares the performance of various face recognition methods. Machine learning, a field of AI, has emerged as an important part of the digitizing approaches that have attracted a lot of interest. The purpose of this work is to provide a high-level overview of several of the most widely utilized and commonly used algorithmic techniques for machine learning currently available. The goal of this work is to help readers make educated decisions about the best algorithm for machine learning they should employ for a given task by highlighting the benefits and drawbacks of each method from an implementation point of view.