4 papers
Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?
Pedro C. Vieira, Pedro Ribeiro, Viacheslav Borovitskiy
While deep ensembles are widely considered to be the default method for uncertainty quantification in deep learning, their effectiveness for graph-structured data is often simply a…
Error-Correction Transitions in Finite-Depth Quantum Channels
Arman Sauliere, Guglielmo Lami, Pedro Ribeiro +2
We study error correction type protocols in which a quantum channel encodes logical information into an enlarged Hilbert space. Specifically, we consider channels realized by one d…
Random matrix perspective on probabilistic error cancellation
Leonhard Moske, Pedro Ribeiro, Tomaž Prosen +3
Probabilistic error cancellation is an attempt to reverse the effect of dissipative noise channels on quantum computers by applying unphysical channels after the execution of a qua…
Fidelity decay and error accumulation in random quantum circuits
Nadir Samos Sáenz de Buruaga, RafaÅ BistroÅ, Marcin RudziÅski +3
We present a comprehensive analysis of fidelity decay and error accumulation in faulty quantum circuit models. Our work devises an analytical bound for the average fidelity between…