CLP-ZNE performs zero-noise extrapolation by averaging over cyclic permutations of circuit layouts, requiring O(n) executions for 1D connectivity and at most O(n^2) for arbitrary connectivity, and reduces errors by an order of magnitude in n=12 qubit benchmarks modeled on IBM Torino hardware.
Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware.Quantum, 6:870, 2022
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.
citing papers explorer
-
Zero-Noise Extrapolation via Cyclic Permutations of Quantum Circuit Layouts
CLP-ZNE performs zero-noise extrapolation by averaging over cyclic permutations of circuit layouts, requiring O(n) executions for 1D connectivity and at most O(n^2) for arbitrary connectivity, and reduces errors by an order of magnitude in n=12 qubit benchmarks modeled on IBM Torino hardware.
-
Quantum End-to-End Learning for Contextual Combinatorial Optimization
QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.