A reduction from weak agnostic learning of class C to efficient tomography of states with bounded l1-extent w.r.t. C, with a concrete algorithm for stabilizer states running in poly(n, (ξ/ε)^log(ξ/ε)) time.
Ignacio , date =
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 4roles
background 1polarities
background 1representative citing papers
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.
Introduces a parallelizable hybrid tensor network algorithm for time-evolving matrix product states that combines classical BUG integration with quantum methods without synchronization barriers.
A comprehensive review of scaling paths for superconducting quantum computers, with resource and sensitivity analyses for utility-scale applications under realistic error distributions.
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.
-
How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits
A comprehensive review of scaling paths for superconducting quantum computers, with resource and sensitivity analyses for utility-scale applications under realistic error distributions.