Quantum-dot based photonic quantum networks

Quantum dots embedded in photonic nanostructures have in recent years proven to be a very powerful solid-state platform for quantum optics experiments. The combination of near-unity radiative coupling of a single quantum dot to a photonic mode and the ability to eliminate decoherence processes imply that an unprecedented light-matter interface can be obtained. As a result, high-cooperativity photon-emitter quantum interfaces can be constructed opening a path-way to deterministic photonic quantum gates for quantum-information processing applications. In the present manuscript, I review current state-of-the-art on quantum dot devices and their applications for quantum technology. The overarching long-term goal of the research field is to construct photonic quantum networks where remote entanglement can be distributed over long distances by photons

All-optical spiking neurosynaptic networks with self-learning capabilities

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record.Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. We exploit wavelength division multiplexing techniques to implement a scalable circuit architecture for photonic neural networks, successfully demonstrating pattern recognition directly in the optical domain. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.Engineering and Physical Sciences Research Council (EPSRC)European CommissionDeutsche Forschungsgemeinschaft (DFG

Architecture, design, and modeling of the OPSnet asynchronous optical packet switching node

An all-optical packet-switched network supporting multiple services represents a long-term goal for network operators and service providers alike. The EPSRC-funded OPSnet project partnership addresses this issue from device through to network architecture perspectives with the key objective of the design, development, and demonstration of a fully operational asynchronous optical packet switch (OPS) suitable for 100 Gb/s dense-wavelength-division multiplexing (DWDM) operation. The OPS is built around a novel buffer and control architecture that has been shown to be highly flexible and to offer the promise of fair and consistent packet delivery at high load conditions with full support for quality of service (QoS) based on differentiated services over generalized multiprotocol label switching

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure

High-Speed and Energy-Efficient Non-Volatile Silicon Photonic Memory Based on Heterogeneously Integrated Memresonator

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs). However, these circuits are constrained by the limited tuning speed and large power consumption of the phase shifters used. In this paper, introduced for the first time are memresonators, or memristors heterogeneously integrated with silicon photonic microring resonators, as phase shifters with non-volatile memory. These devices are capable of retention times of 12 hours, switching voltages lower than 5 V, an endurance of 1,000 switching cycles. Also, these memresonators have been switched using voltage pulses as short as 300 ps with a record low switching energy of 0.15 pJ. Furthermore, these memresonators are fabricated on a heterogeneous III-V/Si platform capable of integrating a rich family of active, passive, and non-linear optoelectronic devices, such as lasers and detectors, directly on-chip to enable in-memory photonic computing and further advance the scalability of integrated photonic processor circuits

MEMS for Photonic Integrated Circuits

The field of microelectromechanical Systems (MEMS) for photonic integrated circuits (PICs) is reviewed. This field leverages mechanics at the nanometer to micrometer scale to improve existing components and introduce novel functionalities in PICs. This review covers the MEMS actuation principles and the mechanical tuning mechanisms for integrated photonics. The state of the art of MEMS tunable components in PICs is quantitatively reviewed and critically assessed with respect to suitability for large-scale integration in existing PIC technology platforms. MEMS provide a powerful approach to overcome current limitations in PIC technologies and to enable a new design dimension with a wide range of applications

Nanomechanical single-photon routing

The merger between integrated photonics and quantum optics promises new opportunities within photonic quantum technology with the very significant progress on excellent photon-emitter interfaces and advanced optical circuits. A key missing functionality is rapid circuitry reconfigurability that ultimately does not introduce loss or emitter decoherence, and operating at a speed matching the photon generation and quantum memory storage time of the on-chip quantum emitter. This ambitious goal requires entirely new active quantum-photonic devices by extending the traditional approaches to reconfigurability. Here, by merging nano-optomechanics and deterministic photon-emitter interfaces we demonstrate on-chip single-photon routing with low loss, small device footprint, and an intrinsic time response approaching the spin coherence time of solid-state quantum emitters. The device is an essential building block for constructing advanced quantum photonic architectures on-chip, towards, e.g., coherent multi-photon sources, deterministic photon-photon quantum gates, quantum repeater nodes, or scalable quantum networks.Comment: 7 pages, 3 figures, supplementary informatio