509 citations · 3.2k across the 61 of their papers we have counts for
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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…
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…
Importance of Kernel Bandwidth in Quantum Machine Learning
Ruslan Shaydulin, Stefan M. Wild
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of…
Exploiting Symmetry Reduces the Cost of Training QAOA
Ruslan Shaydulin, Stefan M. Wild
A promising approach to the practical application of the Quantum Approximate Optimization Algorithm (QAOA) is finding QAOA parameters classically in simulation and sampling the sol…
Origins and optimization of entanglement in plasmonically coupled quantum dots
Matthew Otten, Jeffrey Larson, Misun Min +3
A system of two or more quantum dots interacting with a dissipative plasmonic nanostructure is investigated in detail by using a cavity quantum electrodynamics approach with a mode…