Graph sparsification and decomposition reduce worst-case H_Ising pulses from O(n²) to O(n log(n/ε)) and Pauli-X flips from O(n²) to O(n log(n/ε)/ε²) for (1-ε) Max-Cut approximation in trapped-ion QAOA compilations.
Quantum Computing in the NISQ era and beyond
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A layered resource estimation framework applied to three quantum applications shows practical advantage requires 10^5-10^6 physical qubits, driven by size, speed, and controllability.
Hybrid benchmarking of quantum BFS inside Dinic's algorithm on classical max-flow instances shows that practical speedups would require physically impossible quantum gate speeds.
citing papers explorer
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Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
Graph sparsification and decomposition reduce worst-case H_Ising pulses from O(n²) to O(n log(n/ε)) and Pauli-X flips from O(n²) to O(n log(n/ε)/ε²) for (1-ε) Max-Cut approximation in trapped-ion QAOA compilations.
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Assessing requirements to scale to practical quantum advantage
A layered resource estimation framework applied to three quantum applications shows practical advantage requires 10^5-10^6 physical qubits, driven by size, speed, and controllability.
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Use case study: benchmarking quantum breadth-first search for maximum flow problems
Hybrid benchmarking of quantum BFS inside Dinic's algorithm on classical max-flow instances shows that practical speedups would require physically impossible quantum gate speeds.