activity
20192026
most citedAbsence of Barren Plateaus in Quantum Convolutional Neural Networks

361 citations · 493 across the 7 of their papers we have counts for

collaborators

8 papers

quant-ph2026

Polynomial-time simulation of non-Clifford quantum error correction

Serban Cercelescu, Mark Koch, Arthur Pesah

We show that every intermediate state of a broad class of non-Clifford quantum error-correction circuits is a third-order phase-polynomial state, even under stochastic Pauli noise.…

quant-ph2024★ 2 cited

Quantum Rainbow Codes: Achieving Linear Rate, Growing Distance and Transversal Non-Clifford Gates with Generalised Colour Codes

Thomas R. Scruby, Arthur Pesah, Mark Webster

We introduce rainbow codes, a novel class of quantum error correcting codes generalising colour codes and pin codes. Rainbow codes can be defined on any -dimensional simplicial…

quant-ph2023★ 4 cited

The domain wall color code

Konstantin Tiurev, Arthur Pesah, Peter-Jan H. S. Derks +4

We introduce the domain wall color code, a new variant of the quantum error-correcting color code that exhibits exceptionally high code-capacity error thresholds for qubits subject…

quant-ph2022★ 20 cited

Tailoring three-dimensional topological codes for biased noise

Eric Huang, Arthur Pesah, Christopher T. Chubb +2

Tailored topological stabilizer codes in two dimensions have been shown to exhibit high storage threshold error rates and improved subthreshold performance under biased Pauli noise…

quant-ph2021

Noise-Assisted Variational Quantum Thermalization

Jonathan Foldager, Arthur Pesah, Lars Kai Hansen

Preparing thermal states on a quantum computer can have a variety of applications, from simulating many-body quantum systems to training machine learning models. Variational circui…

quant-ph2020★ 361 cited

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…

quant-ph2020★ 105 cited

Quantum Machine Learning in High Energy Physics

Wen Guan, Gabriel Perdue, Arthur Pesah +4

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early…

stat.ML2019★ 1 cited

Recurrent machines for likelihood-free inference

Arthur Pesah, Antoine Wehenkel, Gilles Louppe

Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a…