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.
On the practical usefulness of the hardware efficient ansatz.Quantum, 8:1395
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Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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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.
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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.