QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.
Zhanget al., Nature Communications17(2025), 10.1038/s41467-025-67768-4
3 Pith papers cite this work. Polarity classification is still indexing.
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Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.
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Quantum-Classical Auxiliary-Field Quantum Monte Carlo at the Edge of Practicability
QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.
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Profiling the Effective Limits of Error Mitigation via Circuit Replication
Circuit replication reduces result variability in QAOA but also lowers inference strength, with effects differing between small and large graphs under real-world noise.
- Hybrid physical/logical zero-noise extrapolation with limited logical executions