The paper derives QAOA parameters from the infinite regular tree limit using tensor networks, so quantum hardware is only needed to sample the final state.
Especially for variational algorithms, where the parameter optimization relies on the accurate evaluation of the loss function, such effects can corrupt the result
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit
The paper derives QAOA parameters from the infinite regular tree limit using tensor networks, so quantum hardware is only needed to sample the final state.