ADsafety: Type-Based Verification of JavaScript Sandboxing

Web sites routinely incorporate JavaScript programs from several sources into a single page. These sources must be protected from one another, which requires robust sandboxing. The many entry-points of sandboxes and the subtleties of JavaScript demand robust verification of the actual sandbox source. We use a novel type system for JavaScript to encode and verify sandboxing properties. The resulting verifier is lightweight and efficient, and operates on actual source. We demonstrate the effectiveness of our technique by applying it to ADsafe, which revealed several bugs and other weaknesses.Comment: in Proceedings of the USENIX Security Symposium (2011

Exploiting symmetries in nuclear Hamiltonians for ground state preparation

The Lipkin and Agassi models are simplified nuclear models that provide natural test beds for quantum simulation methods. Prior work has investigated the suitability of the Variational Quantum Eigensolver (VQE) to find the ground state of these models. There is a growing awareness that if VQE is to prove viable, we will need problem inspired ans\"{a}tze that take into account the symmetry properties of the problem and use clever initialization strategies. Here, by focusing on the Lipkin and Agassi models, we investigate how to do this in the context of nuclear physics ground state problems. We further use our observations to discus the potential of new classical, but quantum-inspired, approaches to learning ground states in nuclear problems.Comment: 7 pages, 4 figure

Runnin\u27 Wild

https://digitalcommons.library.umaine.edu/mmb-vp/6157/thumbnail.jp

Runnin\u27 Wild! / music by Harrington Gibbs and Leo Wood; words by Joe Gray

Cover: drawing of a running man; Publisher: Leo Feist Inc. (New York)https://egrove.olemiss.edu/sharris_d/1040/thumbnail.jp

An Extension to the Frenet-Serret and Bishop Invariant Extended Kalman Filters for Tracking Accelerating Targets

This paper presents an extension to the original Frenet-Serret and Bishop frame target models used in the invariant extended Kalman filter (IEKF) to account for tangential accelerations for highly-manoeuvrable targets. State error propagation matrices are derived for both IEKFs and used to build the accelerating Frenet-Serret (FSa-LIEKF) and Bishop (Ba-LIEKF) algorithms. The filters are compared to the original Frenet-Serret and Bishop algorithms in a tracking scenario featuring a target performing a series of complex manoeuvres. The accelerating forms of the LIEKF are shown to improve velocity estimation during non-constant velocity trajectory segments at the expense of increased noise during simpler manoeuvres

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are assumed to be drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a distribution different from the training distribution. In this work, we prove out-of-distribution generalization for the task of learning an unknown unitary using a QNN and for a broad class of training and testing distributions. In particular, we show that one can learn the action of a unitary on entangled states using only product state training data. We numerically illustrate this by showing that the evolution of a Heisenberg spin chain can be learned using only product training states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics using near term quantum computers and quantum experiments, and further opens up new methods for both the classical and quantum compilation of quantum circuits.Comment: 7 pages (main body) + 14 pages (references and appendix); 4+1 figure

Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota