The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3representative citing papers
Postselected sBs stabilization plus repeated finite-energy measurements yield single-mode GKP SPAM error below 10^{-3} (two orders better than prior art) while remaining compatible with autonomous QEC.
Proposes multi-component bridge states outside cat code space for syndrome extraction in teleportation-based cat code QEC when nonlinear interactions are limiting.
citing papers explorer
-
Measurement-based quantum machine learning
The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
-
Quantum error correction of a grid-state qubit with state preparation and measurement errors below $10^{-3}$
Postselected sBs stabilization plus repeated finite-energy measurements yield single-mode GKP SPAM error below 10^{-3} (two orders better than prior art) while remaining compatible with autonomous QEC.
-
Use of Faulty States in Cat-Code Error Correction
Proposes multi-component bridge states outside cat code space for syndrome extraction in teleportation-based cat code QEC when nonlinear interactions are limiting.