3 papers
quant-ph2025
Quantum advantage without exponential concentration: Trainable kernels for symmetry-structured data
Laura J. Henderson, Kerstin Beer, Salini Karuvade +3
Quantum kernel methods promise enhanced expressivity for learning structured data, but their usefulness has been limited by kernel concentration and barren plateaus. Both effects a…
quant-ph2025
Characterizing noisy quantum computation with imperfectly addressed errors
Riddhi S. Gupta, Salini Karuvade, Kerstin Beer +2
Quantum protocols on hardware are subject to noise that prohibits performance. Protocols for addressing errors, such as error correction or error mitigation, may fail to combat err…
quant-ph2024
Quantum Kernel Machine Learning With Continuous Variables
Laura J. Henderson, Rishi Goel, Sally Shrapnel
The popular qubit framework has dominated recent work on quantum kernel machine learning, with results characterising expressivity, learnability and generalisation. As yet, there i…