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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

classification quant-ph
keywords errorestimatorextrapolationlocalmitigationaccountargumentsbehavior
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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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Forward citations

Cited by 5 Pith papers

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

  1. Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

    stat.ML 2026-04 unverdicted novelty 7.0 of 10

    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.

  2. Faster Probabilistic Error Cancellation

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A binomial-expansion reformulation of PEC, with deterministic shot allocation and truncation-based bias control, reduces the sampling overhead of quantum error mitigation.

  3. Reduced Sampling Overhead for Probabilistic Error Cancellation by Pauli Error Propagation

    quant-ph 2024-12 accept novelty 6.0 of 10

    Merging and propagating Pauli error corrections through Clifford circuits reduces the sampling overhead of probabilistic error cancellation, often by orders of magnitude.

  4. Extension of Clifford Data Regression Methods for Quantum Error Mitigation

    quant-ph 2024-11 conditional novelty 6.0 of 10

    Two new feature maps for Clifford Data Regression, geometric and insertion, with the insertion variants reducing RMSE in small noisy-circuit simulations.

  5. Noisy Monitored Quantum Circuits

    quant-ph 2025-12 accept novelty 2.0 of 10

    A review showing that in noisy monitored quantum circuits, any noise enforces area-law entanglement with characteristic q^{-1/3} scaling and noise-correlation-dependent information-protection timescales.

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