1,587 research outputs found
Optical ground receivers for satellite based quantum communications
Cryptography has always been a key technology in security, privacy and defence.
From ancient Roman times, where messages were sent cyphered with simple encoding techniques, to modern times and the complex security protocols of the Internet.
During the last decades, security of information has been assumed, since classical
computers do not have the power to break the passwords used every day (if they are
generated properly). However, in 1984, a new threat emerged when Peter Shor presented the Shor’s algorithm, an algorithm that could be used in quantum computers
to break many of the secure communication protocols nowadays. Current quantum
computers are still in their early stages, with not enough qubits to perform this
algorithm in reasonable times. However, the threat is present, not future, since the
messages that are being sent by important institutions can be stored, and decoded
in the future once quantum computers are available.
Quantum key distribution (QKD) is one of the solutions proposed for this threat,
and the only one mathematically proven to be secure with no assumptions on the
eavesdropper power. This optical technology has recently gained interest to be performed with satellite communications, the main reason being the relative ease to
deploy a global network in this way. In satellite QKD, the parameter space and
available technology to optimise are very big, so there is still a lot of work to be
done to understand which is the optimal way to exploit this technology.
This dissertation investigates one of these parameters, the encoding scheme.
Most satellite QKD systems use polarisation schemes nowadays. This thesis presents
for the first time an experimental work of a time-bin encoding scheme for free-space
receivers within a full QKD system in the second chapter. The third and fourth
chapter explore the advantages of having multi-protocol free-space receivers that
can boost the interoperability between systems, polarisation filtering techniques to
reduce background. Finally, the last chapter presents a new technology that can
help increase communications rates
Coexistence of multiuser entanglement distribution and classical light in optical fiber network with a semiconductor chip
Building communication links among multiple users in a scalable and robust
way is a key objective in achieving large-scale quantum networks. In realistic
scenario, noise from the coexisting classical light is inevitable and can
ultimately disrupt the entanglement. The previous significant fully connected
multiuser entanglement distribution experiments are conducted using dark fiber
links and there is no explicit relation between the entanglement degradations
induced by classical noise and its error rate. Here we fabricate a
semiconductor chip with a high figure-of-merit modal overlap to directly
generate broadband polarization entanglement. Our monolithic source maintains
polarization entanglement fidelity above 96% for 42 nm bandwidth with a
brightness of 1.2*10^7 Hz/mW. We perform a continuously working quantum
entanglement distribution among three users coexisting with classical light.
Under finite-key analysis, we establish secure keys and enable images
encryption as well as quantum secret sharing between users. Our work paves the
way for practical multiparty quantum communication with integrated photonic
architecture compatible with real-world fiber optical communication network
Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
In the rapid evolution of next-generation brain-inspired artificial
intelligence and increasingly sophisticated electromagnetic environment, the
most bionic characteristics and anti-interference performance of spiking neural
networks show great potential in terms of computational speed, real-time
information processing, and spatio-temporal information processing. Data
processing. Spiking neural network is one of the cores of brain-like artificial
intelligence, which realizes brain-like computing by simulating the structure
and information transfer mode of biological neural networks. This paper
summarizes the strengths, weaknesses and applicability of five neuronal models
and analyzes the characteristics of five network topologies; then reviews the
spiking neural network algorithms and summarizes the unsupervised learning
algorithms based on synaptic plasticity rules and four types of supervised
learning algorithms from the perspectives of unsupervised learning and
supervised learning; finally focuses on the review of brain-like neuromorphic
chips under research at home and abroad. This paper is intended to provide
learning concepts and research orientations for the peers who are new to the
research field of spiking neural networks through systematic summaries
Pulsed Free Space Photonic Vector Network Analyzers
Terahertz (THz) radiation (0.1–10 THz) has demonstrated great significance in a wide range of interdisciplinary applications due to its unique properties such as the capacity to penetrate optically opaque materials without ionizing effect, superior spatial resolution as compared to the microwave domain for imaging or ability to identify a vast array of molecules using THz fingerprinting. Advancements in generation and detection techniques, as well as the necessities of application-driven research and industry, have created a substantial demand for THz-range
devices and components. However, progress in the development of THz components is hampered by a lack of efficient and affordable characterization systems, resulting in limited development in THz science and technology.
Vector Network Analyzers (VNAs) are highly sophisticated well-established characterization instruments in the microwave bands, which are now employed in the lower end of the THz spectrum (up to 1.5 THz) using frequency extender modules. These modules are extremely expensive, and due to the implementation of hollow metallic waveguides for their configuration, they are narrowband, requiring at least six modules to achieve a frequency coverage of 0.2–1.5 THz. Moreover, they are susceptible to problems like material losses, manufacturing and alignment tolerances etc., making them less than ideal for fast, broadband investigation.
The main objective of this thesis is to design a robust but cost-effective characterization system based on a photonic method that can characterize THz components up to several THz in a single configuration. To achieve this, we design architectures for the Photonic Vector Network Analyzer (PVNA) concept, incorporating ErAs:In(Al)GaAs-based photoconductive sources and ErAs:InGaAs-based photoconductive receivers, driven with a femtosecond pulsed laser operating at 1550 nm. The broadband photonic devices replace narrowband electronic ones in order to record the Scattering (S)-parameters in a free space configuration. Corresponding calibration and data evaluation methods are also developed. Then the PVNAs are configured, and their capabilities are validated by characterizing various THz components, including a THz isolator, a
distributed Bragg Reflector, a Split-Ring Resonator array and a Crossed-Dipole Resonator (CDR) array, in terms of their S-parameters. The PVNAs are also implemented to determine the complex refractive index or dielectric permittivity and physical thickness of several materials in the THz range. Finally, we develop an ErAs:In(Al)GaAs-based THz transceiver and implement it in a PVNA configuration, resulting in a more compact setup that is useful for industrial applications. The feasibility of such systems is also verified by characterizing several THz components.
The configured systems achieve a bandwidth of more than 2.5 THz, exceeding the maximum attainable frequency of the commercial Electronic Vector Network Analyzer (EVNA) extender modules. For the 1.1-1.5 THz band, the dynamic range of 47-35 dB (Equivalent Noise Bandwidth (ENBW) = 9.196 Hz) achieved with the PVNA is comparable to the dynamic range of 45-25 dB (ENBW = 10 Hz) of the EVNA. Both amplitude and phase of the S-parameters, determined by the configured PVNAs, are compared with simulations or theoretical models and showed excellent agreement. The PVNA could discern multi-peak and narrow resonance characteristics despite its lower spectral resolution (∼3-7 GHz) compared to the EVNA. By accurately determining the S-parameters of multiple THz components, the transceiver-based PVNA also demonstrated its exceptional competence.
With huge bandwidth and simpler calibration techniques, the PVNA provides a potential solution to bridge the existing technological gap in THz-range characterization systems and offers a solid platform for THz component development, paving the way for more widespread application of THz technologies in research and industry
Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence
Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these.
However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies.
This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in . This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Pipeline quantum processor architecture for silicon spin qubits
Noisy intermediate-scale quantum (NISQ) devices seek to achieve quantum
advantage over classical systems without the use of full quantum error
correction. We propose a NISQ processor architecture using a qubit `pipeline'
in which all run-time control is applied globally, reducing the required number
and complexity of control and interconnect resources. This is achieved by
progressing qubit states through a layered physical array of structures which
realise single and two-qubit gates. Such an approach lends itself to NISQ
applications such as variational quantum eigensolvers which require numerous
repetitions of the same calculation, or small variations thereof. In exchange
for simplifying run-time control, a larger number of physical structures is
required for shuttling the qubits as the circuit depth now corresponds to an
array of physical structures. However, qubit states can be `pipelined' densely
through the arrays for repeated runs to make more efficient use of physical
resources. We describe how the qubit pipeline can be implemented in a silicon
spin-qubit platform, to which it is well suited to due to the high qubit
density and scalability. In this implementation, we describe the physical
realisation of single and two qubit gates which represent a universal gate set
that can achieve fidelities of , even under typical
qubit frequency variations.Comment: 21 pages (13 for main + 8 for supplement), 9 figures (4 for main + 5
for supplement
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