806 citations · 1.5k across the 10 of their papers we have counts for
10 papers
Resource-efficient quantum eigenvalue transform with commutator scaling
Arul Rhik Mazumder, James D. Watson, Samson Wang
We develop quantum algorithms for estimating properties of general matrix functions of Hermitian matrices, with applications to phase estimation, Green's function evaluation, and e…
Barren Plateaus in Variational Quantum Computing
Martin Larocca, Supanut Thanasilp, Samson Wang +7
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Platea…
A framework of partial error correction for intermediate-scale quantum computers
Nikolaos Koukoulekidis, Samson Wang, Tom O'Leary +3
As quantum computing hardware steadily increases in qubit count and quality, one important question is how to allocate these resources to mitigate the effects of hardware noise. In…
Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra
Samson Wang, Sam McArdle, Mario Berta
We propose a class of randomized quantum algorithms for the task of sampling from matrix functions, without the use of quantum block encodings or any other coherent oracle access t…
Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo +1
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other…
The battle of clean and dirty qubits in the era of partial error correction
Daniel Bultrini, Samson Wang, Piotr Czarnik +4
When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be r…
Subtleties in the trainability of quantum machine learning models
Supanut Thanasilp, Samson Wang, Nhat A. Nghiem +2
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achiev…
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
Samson Wang, Piotr Czarnik, Andrew Arrasmith +3
Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-term quantum advantage. However, recent studies have shown that noise can severely limit the traina…
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
Arthur Pesah, M. Cerezo, Samson Wang +3
Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exp…
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Samson Wang, Enrico Fontana, M. Cerezo +4
Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise on NISQ devices p…