509 citations · 3.2k across the 61 of their papers we have counts for
6 papers · 1 filter
A class of sparse Johnson--Lindenstrauss transforms and analysis of their extreme singular values
Kwassi Joseph Dzahini, Stefan M. Wild
The Johnson--Lindenstrauss (JL) lemma is a powerful tool for dimensionality reduction in modern algorithm design. The lemma states that any set of high-dimensional points in a Eucl…
Numerical evidence against advantage with quantum fidelity kernels on classical data
Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti +3
Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel method…
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
Aleksandra Ćiprijanović, Ashia Lewis, Kevin Pedro +4
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discove…
Stochastic trust-region algorithm in random subspaces with convergence and expected complexity analyses
Kwassi Joseph Dzahini, Stefan M. Wild
This work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random s…
Towards Precise and Accurate Calculations of Neutrinoless Double-Beta Decay: Project Scoping Workshop Report
V. Cirigliano, Z. Davoudi, J. Engel +20
We present the results of a National Science Foundation (NSF) Project Scoping Workshop, the purpose of which was to assess the current status of calculations for the nuclear matrix…
Bandwidth Enables Generalization in Quantum Kernel Models
Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan +2
Quantum computers are known to provide speedups over classical state-of-the-art machine learning methods in some specialized settings. For example, quantum kernel methods have been…