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Locality and Error Mitigation of Quantum Circuits
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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.
Forward citations
Cited by 3 Pith papers
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Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
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.
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Faster Probabilistic Error Cancellation
A binomial-expansion reformulation of PEC, with deterministic shot allocation and truncation-based bias control, reduces the sampling overhead of quantum error mitigation.
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Noisy Monitored Quantum Circuits
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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