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Photonic integrated circuits find ubiquitous use in various technologies, from communication, to computing and sensing, and therefore play a crucial role in the quantum technology counterparts. Several systems are currently under investigation, each showing distinct advantages and drawbacks. For this reason, efforts are made to effectively combine different platforms in order to benefit from their respective strengths. In this work, 3D laser written photonic wire bonds are employed to interface triggered sources of quantum light, based on semiconductor quantum dots embedded into etched microlenses, with low-loss silicon-nitride photonics. Single photons at telecom wavelengths are generated by the In(Ga)As quantum dots which are then funneled into a silicon-nitride chip containing single-mode waveguides and beamsplitters. The second-order correlation function of g(2)(0) = 0.11+/-0.02, measured via the on-chip beamsplitter, clearly demonstrates the transfer of single photons into the silicon-nitride platform. The photonic wire bonds funnel on average 28.6+/-8.8% of the bare microlens emission (NA = 0.6) into the silicon-nitride-based photonic integrated circuit even at cryogenic temperatures. This opens the route for the effective future up-scaling of circuitry complexity based on the use of multiple different platforms.

The ability to resolve photon numbers is crucial in quantum information science and technology, driving the development of detectors with intrinsic photon-number resolving (PNR) capabilities. Although transition edge sensors represent the state-of-the-art in PNR performance, superconducting nanowire single-photon detectors (SNSPDs) offer superior efficiency, speed, noise reduction, and timing precision. Directly inferring photon numbers, however, has only recently become feasible due to advances in readout technology. Despite this, photon-number discrimination remains constrained by the nanowire's electrical properties and readout jitter. In this work, we employ waveguide-integrated SNSPDs and time-resolved measurements to explore how the nanowire kinetic inductance and system jitter affect PNR capabilities. By analyzing the latency time of the photon detection, we can resolve changes in the rising edge of the detection pulse. We find that lower jitter as well as increased kinetic inductance enhances the pulse separation for different photon numbers and improves the PNR capability. Enhancing the kinetic inductance from 165 nH to 872 nH improves PNR quality by 12%, 31% and 23% over the first three photon numbers, though at the cost of reducing the detector's count rate from 165 Mcps to 19 Mcps. Our findings highlight the trade-off between PNR resolution and detector speed., Comment: 21 pages, 7 figures

The Stark effect provides a powerful method to shift the spectra of molecules, atoms and electronic transitions in general, becoming one of the simplest and most straightforward way to tune the frequency of quantum emitters by means of a static electric field. At the same time, in order to reduce the emitter sensitivity to charge noise, inversion symmetric systems are typically designed, providing a stable emission frequency, with a quadratic-only dependence on the applied field. However, such nonlinear behaviour might reflect in correlations between the tuning ability and unwanted spectral fluctuations. Here, we provide experimental evidence of this trend, using molecular quantum emitters in the solid state cooled down to liquid helium temperatures. We finally combine the electric field generated by electrodes, which results parallel to the molecule induced dipole, to optically excite long-lived charge states, acting in the perpendicular direction. Based on the anisotropy of the molecule's polarizability, our two-dimensional control of the local electric field allows not only to tune the emitter's frequency but also to sensibly suppress the spectral instabilities associated to field fluctuations.

This work demonstrates a fast thermo-optic switching mechanism on silicon nitride on insulator platform leveraging parity-time symmetry breaking. The cladding-free design enables low-loss optical propagation in a partially metal-covered waveguide, with the same metal layer serving as an integrated heater for rapid phase tuning. The fabricated device exhibits an 8.5 {\mu}s rise time for a {\pi} phase shift, despite the weak thermo-optic coefficient in silicon nitride. Additionally, the impact of thermal cross-talk is investigated and an insertion loss as low as 0.39 dB for a 100-{\mu}m-long heater-waveguide section is demonstrated., Comment: 15 pages, 6 figures

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

Squeezed states are essential for continuous variable (CV) quantum information processing, with wide-ranging applications in computing, sensing and communications. Integrated photonic circuits provide a scalable, convenient platform for building large CV circuits. Thin-film Lithium Niobate (TFLN) is particularly promising due to its low propagation loss, efficient parametric down conversion, and fast electro-optical modulation. In this work, we demonstrate a squeezed light source on an integrated TFLN platform, achieving a measured shot noise reduction of 0.46 dB using modal phase matching and grating couplers with an efficiency of up to -2.2 dB. The achieved squeezing is comparable to what has been observed using more complex circuitry based on periodic poling. The simpler design allows for compact, efficient and reproducible sources of squeezed light., Comment: 6 pages, 6 figures

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges to traditional processors. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. Since both the chaotic light source and the photonic crossbar support multiple independent computational wavelength channels, we sample from the output distributions in parallel at a sampling rate of 70.4 GS/s, limited only by the electronic interface. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling.

Lattice field theory (LFT) is the standard non-perturbative method to perform numerical calculations of quantum field theory. However, the typical bottleneck of fermionic lattice calculations is the inversion of the Dirac matrix. This inversion is solved by iterative methods, like the conjugate gradient algorithm, where matrix-vector multiplications (MVMs) are the main operation. Photonic integrated circuits excel in performing quick and energy-efficient MVMs, but at the same time, they are known to have low accuracy. This can be overcome by using mixed precision methods. In this paper, we explore the idea of using photonic technology to fulfil the demand for computational power of fermionic lattice calculations. These methods have the potential to reduce computation costs by one order of magnitude. Because of the hybrid nature of these methods, we call these 'photonic accelerated inverters (PAIs)'., Comment: 10 pages, 8 figures

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains do. In this work, we experimentally demonstrate these properties occurring in arrays of photonic neurons. Importantly, this is realised autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, we are able to achieve a performance of 98.2% on the MNIST task, a popular benchmark task looking at classification of written digits. The plastic nodes consist of silicon photonics microring resonators covered by a patch of phase-change material that implements nonvolatile memory. The system is compact, robust, and straightforward to scale up through the use of multiple wavelengths. Moreover, it constitutes a unique platform to test and efficiently implement biologically plausible learning schemes at a high processing speed.

Nanophotonic quantum devices can significantly boost light-matter interaction which is important for applications such as quantum networks. Reaching a high interaction strength between an optical transition of a spin system and a single mode of light is an essential step which demands precise control over all degrees of freedom of the optical coupling. While current devices have reached a high accuracy of emitter positioning, the placement process remains overall statistically, reducing the device fabrication yield. Furthermore, not all degrees of freedom of the optical coupling can be controlled limiting the device performance. Here, we develop a hybrid approach based on negatively-charged silicon-vacancy center in nanodiamonds coupled to a mode of a Si$_3$N$_4$-photonic crystal cavity, where all terms of the coupling strength can be controlled individually. We use the frequency of coherent Rabi-oscillations and line-broadening as a measure of the device performance. This allows for iterative optimization of the position and the rotation of the dipole with respect to individual, preselected modes of light. Therefore, our work marks an important step for optimization of hybrid quantum photonics and enables to align device simulations with real device performance., Comment: 20 pages, 7 figures

Hybrid quantum photonic systems connect classical photonics to the quantum world and promise to deliver efficient light-matter quantum interfaces while leveraging the advantages of both, the classical and the quantum, subsystems. However, combining efficient, scalable photonics and solid state quantum systems with desirable optical and spin properties remains a formidable challenge. In particular the access to individual spin states and coherent mapping to photons remains unsolved for these systems. In this letter, we demonstrate all-optical initialization and readout of the electronic spin of a negatively-charged silicon-vacancy center in a nanodiamond coupled to a silicon nitride photonic crystal cavity. We characterize relevant parameters of the coupled emitter-cavity system and determine the silicon-vacancy center's spin-relaxation and spin-decoherence rate. Our results mark an important step towards the realization of a hybrid spin-photon interface based on silicon nitride photonics and the silicon-vacancy center's electron spin in nanodiamonds with potential use for quantum networks, quantum communication and distributed quantum computation., Comment: 6 pages, 4 figures

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ per dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (greater than 87.36) that leads to an enhanced computing accuracy (standard deviation less than 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

Efficient fiber-to-chip couplers for multi-port access to photonic integrated circuits are paramount for a broad class of applications, ranging, e.g., from telecommunication to photonic computing and quantum technologies. While grating-based approaches are convenient for out-of-plane access and often desirable from a packaging point of view, on low-index photonic platforms, such as silicon nitride or thin-film lithium niobate, the limited grating strength has thus far hindered the achievement of coupling efficiencies comparable to the ones attainable in silicon photonics. Here we present a flexible strategy for the realization of highly efficient grating couplers on low-index photonic platforms. To simultaneously reach a high scattering efficiency and a near-unitary modal overlap with optical fibers, we make use of self-imaging gratings designed with a negative diffraction angle. To ensure high directionality of the diffracted light, we take advantage of a metal back-reflector patterned underneath the grating structure by cryogenic deep reactive ion etching of the silicon handle. Using silicon nitride as a testbed material, we experimentally demonstrate coupling efficiency up to -0.55 dB in the telecom C-band with near unity chip-scale device yield.

Scalable photonic quantum computing architectures pose stringent requirements on photonic processing devices. The need for low-loss high-speed reconfigurable circuits and near-deterministic resource state generators are some of the most challenging requirements. Here we develop an integrated photonic platform based on thin-film lithium niobate and interface it with deterministic solid-state single-photon sources based on quantum dots in nanophotonic waveguides. The generated photons are processed with low-loss circuits programmable at speeds of several GHz. We realize a variety of key photonic quantum information processing functionalities with the high-speed circuits, including on-chip quantum interference, photon demultiplexing, and reprogrammability of a four-mode universal photonic circuit. These results show a promising path forward for scalable photonic quantum technologies by merging integrated photonics with solid-state deterministic photon sources in a heterogeneous approach to scaling up.

This chapter covers recent developments in the field of hybrid quantum photonics based on color centers in nanodiamonds and Si3N4-photonics towards a technology platform with applications in quantum information processing and quantum information distribution. The methodological approach can be divided in three main tasks. First, the fabrication and optimization of Si3N4-photonics. Second, the creation, characterization and control of color centers in nanodiamonds. Third, the assembly of hybrid quantum photonics by integrating the nanodiamonds into the photonic structures. One focus will be the efficient interfacing of the color centers done by optimizing the optical coupling. The chapter describes recent progress in all three steps and summarizes the established hybrid platform. We believe, that the hybrid approach provides a promising path to realize quantum photonic applications, such as quantum networks or quantum repeaters, in the near future., Comment: 55 pages, 28 figures

Superconducting nanowire single-photon detectors are an enabling technology for modern quantum information science and are gaining attractiveness for the most demanding photon counting tasks in other fields. Embedding such detectors in photonic integrated circuits enables additional counting capabilities through nanophotonic functionalization. Here we show how a scalable number of waveguide-integrated superconducting nanowire single-photon detectors can be interfaced with independent fiber optic channels on the same chip. Our plug-and-play detector package is hosted inside a compact and portable closed-cycle cryostat providing cryogenic signal amplification for up to 64 channels. We demonstrate state-of-the-art photon counting performance with up to 60 % system detection efficiency and down to 26.0 ps timing accuracy for individually addressable detectors. Our multi-channel single photon receiver offers exciting measurement capabilities for future quantum communication, remote sensing and imaging applications., Comment: 17 pages, 26 Figures

The field of quantum information processing offers secure communication protected by the laws of quantum mechanics and is on the verge of finding wider application for information transfer of sensitive data. To overcome the obstacle of inadequate cost-efficiency, extensive research is being done on the many components required for high data throughput using quantum key distribution (QKD). Aiming for an application-oriented solution, we report on the realization of a multichannel QKD system for plug-and-play high-bandwidth secure communication at telecom wavelength. For this purpose, a rack-sized multichannel superconducting nanowire single photon detector (SNSPD) system, as well as a highly parallelized time-correlated single photon counting (TCSPC) unit have been developed and linked to an FPGA-controlled QKD evaluation setup allowing for continuous operation and achieving high secret key rates using a coherent-one-way protocol., Comment: 13 pages, 6 figures

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges. We hope that this Roadmap will be a useful resource to readers outside this field, for those who are just entering the field, and for those who are well established in the neuromorphic community. https://doi.org/10.1088/2634-4386/ac4a83

Solution processible colloidal quantum dots hold great promise for realizing single-photon sources embedded into scalable quantum technology platforms. However, the high-yield integration of large numbers of individually addressable colloidal quantum dots in a photonic circuit has remained an outstanding challenge. Here, we report on integrating individual colloidal core-shell quantum dots (CQDs) into a nanophotonic network that allows for excitation and efficient collection of single-photons via separate waveguide channels. An iterative electron beam lithography process provides a viable method to position single emitters at predefined positions in a photonic integrated circuit with yield that approaches unity. Our work moves beyond the bulk optic paradigm of confocal microscopy and paves the way for supplying chip-scale quantum networks with single photons from large numbers of simultaneously controllable quantum emitters., Comment: 17 pages, 10 figures

Lithium-Niobate-On-Insulator (LNOI) has emerged as a promising platform in the field of integrated photonics. Nonlinear optical processes and fast electro-optic modulation have been reported with outstanding performance in ultra-low loss waveguides. In order to harness the advantages offered by the LNOI technology, suitable fiber-to-chip interconnects operating at different wavelength ranges are demanded. Here we present easily manufacturable, self-imaging apodized grating couplers, featuring a coupling efficiency of the TE0 mode as high as $\simeq 47.1\%$ at $\lambda$=1550 nm and $\simeq 44.9\%$ at $\lambda$=775 nm. Our approach avoids the use of any metal back-reflector for an improved directivity or multi-layer structures for an enhanced grating strength.

Lithium-Niobate-On-Insulator (LNOI) is emerging as a promising platform for integrated quantum photonic technologies because of its high second-order nonlinearity and compact waveguide footprint. Importantly, LNOI allows for creating electro-optically reconfigurable circuits, which can be efficiently operated at cryogenic temperature. Their integration with superconducting nanowire single-photon detectors (SNSPDs) paves the way for realizing scalable photonic devices for active manipulation and detection of quantum states of light. Here we report the first demonstration of these two key components integrated in a low loss (0.2 dB/cm) LNOI waveguide network. As an experimental showcase of our technology, we demonstrate the combined operation of an electrically tunable Mach-Zehnder interferometer and two waveguide-integrated SNSPDs at its outputs. We show static reconfigurability of our system with a bias-drift-free operation over a time of 12 hours, as well as high-speed modulation at a frequency up to 1 GHz. Our results provide blueprints for implementing complex quantum photonic devices on the LNOI platform.

Spin-based quantum photonics promise to realize distributed quantum computing and quantum networks. The performance depends on efficient entanglement distribution, where the efficiency can be boosted by means of cavity quantum electrodynamics. The central challenge is the development of compact devices with large spin-photon coupling rates and high operation bandwidth. Photonic crystal cavities comprise strong field confinement but put high demands on accurate positioning of an atomic system in the mode field maximum. Color center in diamond, and in particular the negatively-charged Silicon-Vacancy center, emerged as a promising atom-like systems. Large spectral stability and access to long-lived, nuclear spin memories enabled elementary demonstrations of quantum network nodes including memory-enhanced quantum communication. In a hybrid approach, we deterministically place SiV$^-$-containing nanodiamonds inside one hole of a one-dimensional, free-standing, Si$_3$N$_4$-based photonic crystal cavity and coherently couple individual optical transitions to the cavity mode. We optimize the light-matter coupling by utilizing two-mode composition, waveguiding, Purcell-enhancement and cavity resonance tuning. The resulting photon flux is increased by more than a factor of 14 as compared to free-space. The corresponding lifetime shortening to below 460 ps puts the potential operation bandwidth beyond GHz rates. Our results mark an important step to realize quantum network nodes based on hybrid quantum photonics with SiV$^-$- center in nanodiamonds.

Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications even though other learning concepts, in particular backpropagation on artificial neural networks (ANNs) have flourished. However, training using the backpropagation method on 'conventional' ANNs, especially in the form of modern deep neural networks (DNNs), is computationally and energy intensive. Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element. We realize this on an integrated photonic platform using phase-change materials combined with on-chip cascaded directional couplers. We then develop a scaled-up circuit network using our monadic Pavlovian photonic hardware that delivers a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free architectures to address general learning tasks. Our approach reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed, whilst also offering higher bandwidth inherent to our photonic implementation., Comment: 24 pages, 5 figures

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges., Comment: 21 pages, 6 figures

Extinction spectroscopy is a powerful tool for demonstrating the coupling of a single quantum emitter to a photonic structure. However, it can be challenging in all but the simplest of geometries to deduce an accurate value of the coupling efficiency from the measured spectrum. Here we develop a theoretical framework to deduce the coupling efficiency from the measured transmission and reflection spectra without precise knowledge of the photonic environment. We then consider the case of a waveguide interrupted by a transverse cut in which an emitter is placed. We apply that theory to a silicon nitride waveguide interrupted by a gap filled with anthracene that is doped with dibenzoterrylene molecules. We describe the fabrication of these devices, and experimentally characterise the waveguide coupling of a single molecule in the gap., Comment: 10 page article with 3 figures and 6 page supplementary information with 3 figures. Comments welcome

Solid-state single photon sources (SPSs) with narrow line width play an important role in many leading quantum technologies. Within the wide range of SPSs studied to date, single fluorescent molecules hosted in organic crystals stand out as bright, photostable SPSs with a lifetime-limited optical resonance at cryogenic temperatures. Furthermore, recent results have demonstrated that photostability and narrow line widths are still observed from single molecules hosted in a nanocrystalline environment, which paves the way for their integration with photonic circuitry. Polymers offer a compatible matrix for embedding nanocrystals and provide a versatile yet low-cost approach for making nanophotonic structures on chip that guide light and enhance coupling to nanoscale emitters. Here, we present a deterministic nanostructuring technique based on electron-beam lithography for shaping polymers with embedded single molecules. Our approach provides a direct means of structuring the nanoscale environment of narrow line width emitters while preserving their emission properties.

We study an integrated silicon photonic chip, composed of several sub-wavelength ridge waveguides, and immersed in a micro-cell with rubidium vapor. Employing two-photon excitation, including a telecom wavelength, we observe that the waveguide transmission spectrum gets modified when the photonic mode is coupled to rubidium atoms through its evanescent tail. Due to the enhanced electric field in the waveguide cladding, the atomic transition can be saturated at a photon number $\approx$ 80 times less than a free-propagating beam case. The non-linearity of the atom-clad Si-waveguide is about 4 orders of magnitude larger than maximum achievable value in doped Si photonics. The measured spectra corroborate well with a generalized effective susceptibility model that includes the Casimir-Polder potentials, due to the dielectric surface, and the transient interaction between flying atoms and the evanescent waveguide mode. This work paves the way towards a miniaturized, low-power, and integrated hybrid atomic-photonic system compatible with CMOS technologies., Comment: 9 pages, 8 figures, added references for section 1

With the proliferation of ultra-high-speed mobile networks and internet-connected devices, along with the rise of artificial intelligence, the world is generating exponentially increasing amounts of data - data that needs to be processed in a fast, efficient and smart way. These developments are pushing the limits of existing computing paradigms, and highly parallelized, fast and scalable hardware concepts are becoming progressively more important. Here, we demonstrate a computational specific integrated photonic tensor core - the optical analog of an ASIC-capable of operating at Tera-Multiply-Accumulate per second (TMAC/s) speeds. The photonic core achieves parallelized photonic in-memory computing using phase-change memory arrays and photonic chip-based optical frequency combs (soliton microcombs). The computation is reduced to measuring the optical transmission of reconfigurable and non-resonant passive components and can operate at a bandwidth exceeding 14 GHz, limited only by the speed of the modulators and photodetectors. Given recent advances in hybrid integration of soliton microcombs at microwave line rates, ultra-low loss silicon nitride waveguides, and high speed on-chip detectors and modulators, our approach provides a path towards full CMOS wafer-scale integration of the photonic tensor core. While we focus on convolution processing, more generally our results indicate the major potential of integrated photonics for parallel, fast, and efficient computational hardware in demanding AI applications such as autonomous driving, live video processing, and next generation cloud computing services.

Superconducting nanowire single-photon detectors promise efficient (~100%) and fast (~Gcps) detection of light at the single-photon level. They constitute one of the building blocks to realize integrated quantum optical circuits in a waveguide architecture. The optical response of single-photon detectors, however, is limited to measure only the presence of photons. It misses the capability to resolve the spectrum of a possible broadband illumination. In this work, we propose the optical design for a superconducting nanowire single-photon spectrometer in an integrated optical platform. We exploit a cascade of cavities with different resonance wavelengths side-coupled to a photonic crystal bus waveguide. This allows to demultiplex different wavelengths into different spatial regions, where individual superconducting nanowires that measure the presence of single photons are placed next to these cavities. We employ temporal coupled-mode theory to derive the optimal conditions to achieve a high absorption efficiency in the nanowire with fine spectral resolution. It is shown that the use of a mirror at the end of the cascaded system that terminates the photonic crystal bus waveguide increases the absorption efficiency up to unity, in principle, in the absence of loss. The expected response is demonstrated by full-wave simulations for both two-dimensional and three-dimensional structures. Absorption efficiencies of about 80% are achieved both in two-dimensional structures for four cascaded cavities and in three-dimensional structures for two cascaded cavities. The achieved spectral resolution is about 1 nm. We expect that the proposed setup, both analytically studied and numerically demonstrated in this work, offers a great impetus for future quantum nanophotonic on-chip technologies., Comment: 10 pages, 9 figures

We present the first demonstration of an integrated photonic phase-change memory using GeSbTe-225 on silicon-on-insulator and demonstrate reliable multilevel operation with a single programming pulse. We also compare our results on silicon with previous demonstrations on silicon nitride. Crucially, achieving this on silicon enables tighter integration of traditional electronics with photonic memories in future, making phase-change photonic memory a viable and integrable technology.

Modern-day computers use electrical signaling for processing and storing data which is bandwidth limited and power-hungry. These limitations are bypassed in the field of communications, where optical signaling is the norm. To exploit optical signaling in computing, however, new on-chip devices that work seamlessly in both electrical and optical domains are needed. Phase change devices can in principle provide such functionality, but doing so in a single device has proved elusive due to conflicting requirements of size-limited electrical switching and diffraction-limited photonic devices. Here, we combine plasmonics, photonics and electronics to deliver a novel integrated phase-change memory and computing cell that can be electrically or optically switched between binary or multilevel states, and read-out in either mode, thus merging computing and communications technologies.

We showcase an optical random sampling scope that exploits single photon counting and apply it to characterize optical transceivers. We study single photon detectors with a jitter down to 40 ps. The method can be extended beyond 100 GHz., Comment: ECOC 2018 demo session paper

Compact spectrometers based on disordered planar waveguides exhibit a rather high resolution with a relatively small footprint as compared to conventional spectrometers. This is achieved by multiple scattering of light which - if properly engineered - significantly enhances the effective optical path length. Here a design study of random spectrometers for TE- and TM-polarized light is presented that combines the results of Mie theory, multiple-scattering theory and full electromagnetic simulations. It is shown that the performance of such random spectrometers depends on single scattering quantities, notably on the overall scattering efficiency and the asymmetry parameter. Further, the study shows that a well-developed diffusive regime is not required in practice and that a standard integrated-optical layout is sufficient to obtain efficient devices even for rather weakly scattering systems consisting of low index inclusions in high-index matrices such as pores in planar silicon-nitride based waveguides. This allows for both significant reductions in footprint with acceptable losses in resolution and for device operation in the visible and near-infrared frequency range.

Collocated data processing and storage are the norm in biological systems. Indeed, the von Neumann computing architecture, that physically and temporally separates processing and memory, was born more of pragmatism based on available technology. As our ability to create better hardware improves, new computational paradigms are being explored. Integrated photonic circuits are regarded as an attractive solution for on-chip computing using only light, leveraging the increased speed and bandwidth potential of working in the optical domain, and importantly, removing the need for time and energy sapping electro-optical conversions. Here we show that we can combine the emerging area of integrated optics with collocated data storage and processing to enable all-photonic in-memory computations. By employing non-volatile photonic elements based on the phase-change material, Ge2Sb2Te5, we are able to achieve direct scalar multiplication on single devices. Featuring a novel single-shot Write/Erase and a drift-free process, such elements can multiply two scalar numbers by mapping their values to the energy of an input pulse and to the transmittance of the device, codified in the crystallographic state of the element. The output pulse, carrying the information of the light-matter interaction, is the result of the computation. Our all-optical approach is novel, easy to fabricate and operate, and sets the stage for development of entirely photonic computers.