10 papers
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…
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…
Distributed Quantum Property Testing with Communication Constraints
Mina Doosti, Ryan Sweke, Chirag Wadhwa
We introduce a framework for distributed quantum inference under communication constraints. In our model, distributed nodes each receive one copy of an unknown -dimensional…
Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
David Layden, Ryan Sweke, VojtÄch HavlÃÄek +2
Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifica…
On the average-case complexity of learning output distributions of quantum circuits
Alexander Nietner, Marios Ioannou, Ryan Sweke +4
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely…
Learning topological states from randomized measurements using variational tensor network tomography
Yanting Teng, Rhine Samajdar, Katherine Van Kirk +5
Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic meth…