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Found 7 papers, 2 papers with code
Learning to rank quantum circuits for hardware-optimized performance enhancement
We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware.
A Rubik's Cube inspired approach to Clifford synthesis
The problem of decomposing an arbitrary Clifford element into a sequence of Clifford gates is known as Clifford synthesis.
Empirical Evaluation of Physical Adversarial Patch Attacks Against Overhead Object Detection Models
In this work we further test the efficacy of adversarial patch attacks in the physical world under more challenging conditions.
Deep Generative Modeling in Network Science with Applications to Public Policy Research
We then review recent advances in applying deep learning to network data, and show how these methods may be used to address many of the methodological problems we identified.
A Probability Density Theory for Spin-Glass Systems
In general, evaluating the relevant physical and computational properties of such models is difficult due to critical slowing down near a phase transition.
Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
Spin-glasses are universal models that can capture complex behavior of many-body systems at the interface of statistical physics and computer science including discrete optimization, inference in graphical models, and automated reasoning.
Adversarial Examples for Cost-Sensitive Classifiers
Motivated by safety-critical classification problems, we investigate adversarial attacks against cost-sensitive classifiers.
