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Locality and Error Mitigation of Quantum Circuits

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arxiv 2303.06496 v1 pith:4NYK7MX4 submitted 2023-03-11 quant-ph

Locality and Error Mitigation of Quantum Circuits

classification quant-ph
keywords errorestimatorextrapolationlocalmitigationaccountargumentsbehavior
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this work, we study and improve two leading error mitigation techniques, namely Probabilistic Error Cancellation (PEC) and Zero-Noise Extrapolation (ZNE), for estimating the expectation value of local observables. For PEC, we introduce a new estimator that takes into account the light cone of the unitary circuit with respect to a target local observable. Given a fixed error tolerance, the sampling overhead for the new estimator can be several orders of magnitude smaller than the standard PEC estimators. For ZNE, we also use light-cone arguments to establish an error bound that closely captures the behavior of the bias that remains after extrapolation.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    quant-ph 2026-04 conditional novelty 8.0

    MPS TE-PAI achieves unbiased classical time evolution by averaging tensor-network representations of randomized shallow Trotter circuits, yielding lower gate counts per sample and better tolerance to bond-dimension tr...

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  3. Reliable high-accuracy error mitigation for utility-scale quantum circuits

    quant-ph 2025-08 conditional novelty 6.0

    QESEM is a characterization-based error mitigation technique that achieves unbiased estimates with substantially reduced runtime cost compared to probabilistic error cancellation while outperforming zero-noise extrapo...

  4. Near-Term Fermionic Simulation with Subspace Noise Tailored Quantum Error Mitigation

    quant-ph 2025-03 unverdicted novelty 6.0

    SNT merges SV and PEC for subspace-tailored error mitigation in Trotterized FHM simulations, mapping out optimal combinations by hardware quality and shot budget while quantifying when noisy devices could surpass clas...

  5. Computing noise-canceling observables via Pauli propagation

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    stat.ML 2026-04 unverdicted novelty 5.0

    Off-model importance sampling with dominating training laws lets non-Markovian stochastic control and adaptive recalibration reuse one fixed Monte Carlo sample under model uncertainty.

  7. Noisy Monitored Quantum Circuits

    quant-ph 2025-12 accept novelty 2.0

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