Quantum neural networks to simulate many-body quantum systems

We conduct experimental simulations of many body quantum systems using a \emph{hybrid} classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neural network is then trained using variational Monte Carlo assisted by a D-Wave quantum sampler to find the ground state energy. Our results clearly demonstrate that already the first generation of quantum computers can be harnessed to tackle non-trivial problems concerning physics of many body quantum systems.Comment: 6 pages, 4 figure

Routes Towards Optical Quantum Technology --- New Architectures and Applications

This thesis is based upon the work I have done during my PhD candidature at Macquarie University. In this work we develop quantum technologies that are directed towards realising a quantum computer. Specifically, we have made many theoretical advancements in a type of quantum information processing protocol called BosonSampling. This device efficiently simulates the interaction of quantum particles called bosons, which no classical computer can efficiently simulate. In this thesis we explore quantum random walks, which are the basis of how the bosons in BosonSampling interfere with each other. We explore implementing BosonSampling using the most readily available photon source technology. We invented a completely new architecture which can implement BosonSampling in time rather than space and has since been used to make the worlds largest BosonSampling experiment ever performed. We look at variations to the traditional BosonSampling architecture by considering other quantum states of light. We show a worlds first application inspired by BosonSampling in quantum metrology where measurements may be made more accurately than with any classical method. Lastly, dealing with BosonSampling, we look at reformulating the formalism of BosonSampling using a quantum optics approach. In addition, but not related to BosonSampling, we show a protocol for efficiently generating large-photon Fock states, which are a type of quantum state of light, that are useful for quantum computation. Also, we show a method for generating a specific quantum state of light that is useful for quantum error correction --- an essential component of realising a quantum computer --- by coupling together light and atoms.Comment: PhD Thesi

NASA Tech Briefs, November 2011

The topics include: 1) Flight Test Results from the Rake Airflow Gage Experiment on the F-15B; 2) Telemetry and Science Data Software System; 3) CropEx Web-Based Agricultural Monitoring and Decision Support; 4) High-Performance Data Analysis Tools for Sun-Earth Connection Missions; 5) Experiment in Onboard Synthetic Aperture Radar Data Processing; 6) Microfabrication of a High-Throughput Nanochannel Delivery/Filtration System; 7) Improved Design and Fabrication of Hydrated-Salt Pills; 8) Monolithic Flexure Pre-Stressed Ultrasonic Horns; 9) Cryogenic Quenching Process for Electronic Part Screening; 10) Broadband Via-Less Microwave Crossover Using Microstrip-CPW Transitions; 11) Wheel-Based Ice Sensors for Road Vehicles; 12) G-DYN Multibody Dynamics Engine; 13) Multibody Simulation Software Testbed for Small-Body Exploration and Sampling; 14) Propulsive Reaction Control System Model; 15) Licklider Transmission Protocol Implementation; 16) Core Recursive Hierarchical Image Segmentation; 17) Two-Stage Centrifugal Fan; 18) Combined Structural and Trajectory Control of Variable-Geometry Planetary Entry Systems; 19) Pressure Regulator With Internal Ejector Circulation Pump, Flow and Pressure Measurement Porting, and Fuel Cell System Integration Options; 20) Temperature-Sensitive Coating Sensor Based on Hematite; 21) Standardization of a Volumetric Displacement Measurement for Two-Body Abrasion Scratch Test Data Analysis; 22) Detection of Carbon Monoxide Using Polymer-Carbon Composite Films; 23) Substituted Quaternary Ammonium Salts Improve Low-Temperature Performance of Double-Layer Capacitors; 24) Sustainably Sourced, Thermally Resistant, Radiation Hard Biopolymer; 25) Integrated Lens Antennas for Multi-Pixel Receivers; 26) 180-GHz Interferometric Imager; 27) Maturation of Structural Health Management Systems for Solid Rocket Motors; 28) Validating Phasing and Geometry of Large Focal Plane Arrays; 29) Transverse Pupil Shifts for Adaptive Optics Non-Common Path Calibration; 30) Qualification of Fiber Optic Cables for Martian Extreme Temperature Environments; 31) Solid-State Spectral Light Source System; 32) Multiple-Event, Single-Photon Counting Imaging Sensor; 33) Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations; and 34) Achieving Exact and Constant Turnaround Ratio in a DDS-Based Coherent Transponder

Automated early plant disease detection and grading system: Development and implementation

As the agriculture industry grows, many attempts have been made to ensure high quality of produce. Diseases and defects found in plants and crops, affect the agriculture industry greatly. Hence, many techniques and technologies have been developed to help solving or reducing the impact of plant diseases. Imagining analysis tools, and gas sensors are becoming more frequently integrated into smart systems for plant disease detection. Many disease detection systems incorporate imaging analysis tools and Volatile Organic Compound (VOC) profiling techniques to detect early symptoms of diseases and defects of plants, fruits and vegetative produce. These disease detection techniques can be further categorized into two main groups; preharvest disease detection and postharvest disease detection techniques. This thesis aims to introduce the available disease detection techniques and to compare it with the latest innovative smart systems that feature visible imaging, hyperspectral imaging, and VOC profiling. In addition, this thesis incorporates the use of image analysis tools and k-means segmentation to implement a preharvest Offline and Online disease detection system. The Offline system to be used by pathologists and agriculturists to measure plant leaf disease severity levels. K-means segmentation and triangle thresholding techniques are used together to achieve good background segmentation of leaf images. Moreover, a Mamdani-Type Fuzzy Logic classification technique is used to accurately categorize leaf disease severity level. Leaf images taken from a real field with varying resolutions were tested using the implemented system to observe its effect on disease grade classification. Background segmentation using k-means clustering and triangle thresholding proved to be effective, even in non-uniform lighting conditions. Integration of a Fuzzy Logic system for leaf disease severity level classification yielded in classification accuracies of 98%. Furthermore, a robot is designed and implemented as a robotized Online system to provide field based analysis of plant health using visible and near infrared spectroscopy. Fusion of visible and near infrared images are used to calculate the Normalized Deference Vegetative Index (NDVI) to measure and monitor plant health. The robot is designed to have the functionality of moving across a specified path within an agriculture field and provide health information of leaves as well as position data. The system was tested in a tomato greenhouse under real field conditions. The developed system proved effective in accurately classifying plant health into one of 3 classes; underdeveloped, unhealthy, and healthy with an accuracy of 83%. A map with plant health and locations is produced for farmers and agriculturists to monitor the plant health across different areas. This system has the capability of providing early vital health analysis of plants for immediate action and possible selective pesticide spraying

A New Strategy for Deep Wide-Field High Resolution Optical Imaging

We propose a new strategy for obtaining enhanced resolution (FWHM = 0.12 arcsec) deep optical images over a wide field of view. As is well known, this type of image quality can be obtained in principle simply by fast guiding on a small (D = 1.5m) telescope at a good site, but only for target objects which lie within a limited angular distance of a suitably bright guide star. For high altitude turbulence this 'isokinetic angle' is approximately 1 arcminute. With a 1 degree field say one would need to track and correct the motions of thousands of isokinetic patches, yet there are typically too few sufficiently bright guide stars to provide the necessary guiding information. Our proposed solution to these problems has two novel features. The first is to use orthogonal transfer charge-coupled device (OTCCD) technology to effectively implement a wide field 'rubber focal plane' detector composed of an array of cells which can be guided independently. The second is to combine measured motions of a set of guide stars made with an array of telescopes to provide the extra information needed to fully determine the deflection field. We discuss the performance, feasibility and design constraints on a system which would provide the collecting area equivalent to a single 9m telescope, a 1 degree square field and 0.12 arcsec FWHM image quality.Comment: 46 pages, 22 figures, submitted to PASP, a version with higher resolution images and other supplementary material can be found at http://www.ifa.hawaii.edu/~kaiser/wfhr

Magnetic biosensors: modelling and simulation

In the past few years, magnetoelectronics has emerged as a promising new platform technology in various biosensors for detection, identification, localisation and manipulation of a wide spectrum of biological, physical and chemical agents. The methods are based on the exposure of the magnetic field of a magnetically labelled biomolecule interacting with a complementary biomolecule bound to a magnetic field sensor. This Review presents various schemes of magnetic biosensor techniques from both simulation and modelling as well as analytical and numerical analysis points of view, and the performance variations under magnetic fields at steady and nonstationary states. This is followed by magnetic sensors modelling and simulations using advanced Multiphysics modelling software (e.g. Finite Element Method (FEM) etc.) and home-made developed tools. Furthermore, outlook and future directions of modelling and simulations of magnetic biosensors in different technologies and materials are critically discussed