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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Noise Resilience Of Variational Quantum Compiling, Kunal Sharma, Sumeet Khatri2, M. Cerezo, Patrick J. Coles
Noise Resilience Of Variational Quantum Compiling, Kunal Sharma, Sumeet Khatri2, M. Cerezo, Patrick J. Coles
Faculty Publications
Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the cost-evaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters …
Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple
Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple
Faculty Publications
Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …
Graph Pebbling Algorithms And Lemke Graphs, Charles A. Cusack, Aaron Green, Airat Bekmetjev, Mark Powers
Graph Pebbling Algorithms And Lemke Graphs, Charles A. Cusack, Aaron Green, Airat Bekmetjev, Mark Powers
Faculty Publications
Given a simple, connected graph, a pebbling configuration (or just configuration) is a function from its vertex set to the nonnegative integers. A pebbling move between adjacent vertices removes two pebbles from one vertex and adds one pebble to the other. A vertex r is said to be reachable from a configuration if there exists a sequence of pebbling moves that places at least one pebble on r. A configuration is solvable if every vertex is reachable. The pebbling number π(G) of a graph G is the minimum integer such that every configuration of size π(G) on G …
A Legal Perspective On The Trials And Tribulations Of Ai: How Artificial Intelligence, The Internet Of Things, Smart Contracts, And Other Technologies Will Affect The Law, Iria Giuffrida, Fredric Lederer, Nicolas Vermeys
A Legal Perspective On The Trials And Tribulations Of Ai: How Artificial Intelligence, The Internet Of Things, Smart Contracts, And Other Technologies Will Affect The Law, Iria Giuffrida, Fredric Lederer, Nicolas Vermeys
Faculty Publications
No abstract provided.
Improving Occupancy Grid Fastslam By Integrating Navigation Sensors, Christopher Weyers, Gilbert L. Peterson
Improving Occupancy Grid Fastslam By Integrating Navigation Sensors, Christopher Weyers, Gilbert L. Peterson
Faculty Publications
When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use …
Identification And Optimization Of Classifier Genes From Multi-Class Earthworm Microarray Dataset, Ying Li, Nan Wang, Chaoyang Zhang, Ping Gong
Identification And Optimization Of Classifier Genes From Multi-Class Earthworm Microarray Dataset, Ying Li, Nan Wang, Chaoyang Zhang, Ping Gong
Faculty Publications
Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes and have used it to profile gene expression in 248 earthworms exposed to TNT, RDX or neither. We assembled a new machine learning pipeline consisting of several well-established feature filtering/selection and classification techniques to analyze the 248-array dataset in order to construct classifier models that …
Electronic Image Stabilization Using Optical Flow With Inertial Fusion, Michael J. Smith, Alexander J. Boxerbaum, Gilbert L. Peterson, Roger D. Quinn
Electronic Image Stabilization Using Optical Flow With Inertial Fusion, Michael J. Smith, Alexander J. Boxerbaum, Gilbert L. Peterson, Roger D. Quinn
Faculty Publications
When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This paper presents a novel electronic image stabilization (EIS) algorithm for highly dynamic mobile robotic platforms. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted using a simulated video truth model (capturing the effects of image translation, rotation, blurring, and moving objects), and live …
Hardness For Explicit State Software Model Checking Benchmarks, Eric G. Mercer, Neha Rungta
Hardness For Explicit State Software Model Checking Benchmarks, Eric G. Mercer, Neha Rungta
Faculty Publications
Directed model checking algorithms focus computation resources in the error-prone areas of concurrent systems. The algorithms depend on some empirical analysis to report their performance gains. Recent work characterizes the hardness of models used in the analysis as an estimated number of paths in the model that contain an error. This hardness metric is computed using a stateless random walk. We show that this is not a good hardness metric because models labeled hard with a stateless random walk metric have easily discoverable errors with a stateful randomized search. We present an analysis which shows that a hardness metric based …
Using Genetic Algorithms To Map First-Principles Results To Model Hamiltonians: Application To The Generalized Ising Model For Alloys, Gus L. W. Hart, Volker Blum, Michael J. Walorski, Alex Zunger
Using Genetic Algorithms To Map First-Principles Results To Model Hamiltonians: Application To The Generalized Ising Model For Alloys, Gus L. W. Hart, Volker Blum, Michael J. Walorski, Alex Zunger
Faculty Publications
The cluster expansion method provides a standard framework to map first-principles generated energies for a few selected configurations of a binary alloy onto a finite set of pair and many-body interactions between the alloyed elements. These interactions describe the energetics of all possible configurations of the same alloy, which can hence be readily used to identify ground state structures and, through statistical mechanics solutions, find finite-temperature properties. In practice, the biggest challenge is to identify the types of interactions which are most important for a given alloy out of the many possibilities. We describe a genetic algorithm which automates this …
Cell Size Dependence Of Transport Coefficients In Stochastic Particle Algorithms, Alejandro Garcia, F. Alexander, B. Alder
Cell Size Dependence Of Transport Coefficients In Stochastic Particle Algorithms, Alejandro Garcia, F. Alexander, B. Alder
Faculty Publications
Using the Green–Kubo theory, the dependence of the viscosity and thermal conductivity on cell size is obtained explicitly for stochastic particle methods such as direct simulation Monte Carlo (DSMC) and its generalization, the consistent Boltzmann algorithm (CBA). These analytical results confirm empirical observations that significant errors occur when the cell dimensions are larger than a mean free path.
Algebraic Geometry For Computer-Aided Geometric Design, Thomas W. Sederberg, Ronald N. Goldman
Algebraic Geometry For Computer-Aided Geometric Design, Thomas W. Sederberg, Ronald N. Goldman
Faculty Publications
Classical algebraic geometry has been virtually ignored in computer-aided geometric design. However, because it deals strictly with algorithms, it is really more suited to this field than is modern algebraic geometry, which introduces abstractions far removed from the algorithmic nature of computer-aided design. This tutorial examines resultants, curve implicitization, curve inversion, and curve intersection. Discussion follows a series of examples simple enough for those with only a modest algebra background to follow.