17 citations · 28 across the 4 of their papers we have counts for
6 papers
Quantum Multiple Kernel Learning
Seyed Shakib Vedaie, Moslem Noori, Jaspreet S. Oberoi +2
Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks. Expressivity…
Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features
Moslem Noori, Seyed Shakib Vedaie, Inderpreet Singh +4
Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing…
The Power of One Qubit in Machine Learning
Roohollah Ghobadi, Jaspreet S. Oberoi, Ehsan Zahedinejhad
Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning al…
A Quantum Annealing-Based Approach to Extreme Clustering
Tim Jaschek, Marko Bucyk, Jaspreet S. Oberoi
Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large num…
Multi-Community Detection in Signed Graphs Using Quantum Hardware
Ehsan Zahedinejad, Daniel Crawford, Clemens Adolphs +1
Signed graphs serve as a primary tool for modelling social networks. They can represent relationships between individuals (i.e., nodes) with the use of signed edges. Finding commun…
Free energy-based reinforcement learning using a quantum processor
Anna Levit, Daniel Crawford, Navid Ghadermarzy +3
Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce fre…