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Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
David Layden, Ryan Sweke, Vojtěch Havlíček +2
Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifica…
Efficient Quantum Gibbs Sampling with Local Circuits
Dominik Hahn, Ryan Sweke, Abhinav Deshpande +1
The problem of simulating the thermal behavior of quantum systems remains a central open challenge in quantum computing. Unlike well-established quantum algorithms for unitary dyna…
Kernel-based dequantization of variational QML without Random Fourier Features
Ryan Sweke, Seongwook Shin, Elies Gil-Fuster
There is currently a huge effort to understand the potential and limitations of variational quantum machine learning (QML) based on the optimization of parameterized quantum circui…
New perspectives on quantum kernels through the lens of entangled tensor kernels
Seongwook Shin, Ryan Sweke, Hyunseok Jeong
Quantum kernel methods are one of the most explored approaches to quantum machine learning. However, the structural properties and inductive bias of quantum kernels are not fully u…