Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.