9 citations · 11 across the 5 of their papers we have counts for
5 papers
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
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…
Virtual distillation with noise dilution
Yong Siah Teo, Seongwook Shin, Hyukgun Kwon +2
Virtual distillation is an error-mitigation technique that reduces quantum-computation errors without assuming the noise type. In scenarios where the user of a quantum circuit is r…
Benchmarking quantum tomography completeness and fidelity with machine learning
Yong Siah Teo, Seongwook Shin, Hyunseok Jeong +8
We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior inf…
Highly accurate Gaussian process tomography with geometrical sets of coherent states
Yong Siah Teo, Kimin Park, Seongwook Shin +2
We propose a practical strategy for choosing sets of input coherent states that are near-optimal for reconstructing single-mode Gaussian quantum processes with output-state heterod…