IBM’s quantum error-mitigation methods can make estimates from selected noisy quantum circuits more accurate, but they often require extra sampling, classical processing, or both. That can mean better estimates at the cost of more runtime. It is progress toward useful quantum computing, not by itself evidence that a quantum computer has achieved broad practical advantage or become fault-tolerant.
What is quantum error mitigation?
Current quantum processors are noisy: operations and measurements can produce results that differ from the ideal circuit’s output. Error mitigation uses information about those errors, additional circuit runs, or classical analysis to reduce their effect on a chosen result.
Mitigation is not the same as fault tolerance. Fault-tolerant computing aims to protect computation through error-correcting codes and sufficiently reliable operations. Mitigation instead works with noisy hardware to improve estimates for particular computations. IBM has described it as a bridge between present-day hardware and future fault-tolerant machines.
The distinction matters because “performance” can refer to three different things:
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- Accuracy: how close an estimated quantity, such as an expectation value, is to the desired ideal result.
- Resource cost: how much additional quantum sampling, processor time, or classical computation is needed to improve that estimate.
- Practical advantage: whether the complete task is better than the strongest relevant classical approach, considering both result quality and total cost.
Mitigation may improve accuracy while increasing the resources needed to obtain it. A more accurate answer to one selected observable does not, on its own, establish an overall advantage.
How do IBM’s mitigation methods work?
Dynamical decoupling: reduce errors during idle periods
Dynamical decoupling (DD) inserts pulse sequences while qubits are idle to counter unwanted interactions during those waits. IBM’s documentation says it is most useful when a circuit has idle gaps. In a densely packed circuit, where qubits are busy most of the time, the added pulses may not help; imperfect pulses can even worsen results.
Zero-noise extrapolation: estimate what a quieter run would produce
Zero-noise extrapolation (ZNE) runs versions of a circuit at amplified noise levels, then extrapolates the results toward a zero-noise estimate. Gate folding is one documented way to amplify noise. The estimate depends on the scaling and extrapolation working well: IBM warns that gate folding can be inaccurate and lead to incorrect results.
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Probabilistic error cancellation: use a noise model and extra sampling
Probabilistic error cancellation (PEC) uses a model of the processor’s noise and additional sampling to estimate idealized outputs. IBM’s 2022 discussion describes PEC as capable of producing clean estimators, while emphasizing runtime overhead as a central cost. The method’s usefulness therefore depends not just on the estimate but also on the resources spent to obtain it.
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Readout mitigation targets errors introduced when the processor measures qubits. IBM’s Qiskit Mitigation documentation lists twirled readout error extinction (TREX) alongside PEC and ZNE. These methods address measurement error rather than serving as a universal correction for every source of circuit noise.
Machine-learning error mitigation: learn corrections from data
Machine-learning quantum error mitigation (ML-QEM) uses classical models trained or calibrated against quantum outcomes. An IBM Research presentation abstract from March 2024 describes simulations and hardware experiments involving up to 100 qubits. Its authors report reduced overhead and accuracy comparable to or better than conventional methods in the tested settings; that result does not guarantee the same performance for other circuits or noise conditions.
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Postselection: discard samples that fail checks
Postselection filters out samples that fail selected checks, such as circuit symmetries, spacetime checks, or non-Markovian error checks. IBM lists these capabilities in its Qiskit Mitigation package. Filtering can improve the retained data’s quality, but the value of the approach depends on the checks and on how many samples are rejected.
What have IBM’s demonstrations established?
ZNE on circuits up to 127 qubits
An IBM Research talk description from February 2024 says its researchers demonstrated ZNE for circuits up to 127 qubits. It attributes improved accuracy of mitigated expectation values to advances in processor coherence and controllable noise scaling. This establishes the width of circuits described in those experiments—not that arbitrary circuits of that size will produce accurate or useful results.
ML-QEM simulations and experiments up to 100 qubits
A separate IBM Research presentation from March 2024 describes ML-QEM simulations and hardware experiments involving up to 100 qubits. The reported reduction in overhead and comparable or better accuracy applies to the model, circuit, and noise conditions studied, rather than to all quantum workloads.
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Why the error model matters
Several mitigation approaches depend on an accurate understanding of hardware noise. A 2025 paper in PRX Quantum by IBM-affiliated researchers addresses the risk that model inaccuracies can undermine mitigation. The authors develop bounds on systematic error when the model is violated and test their methodology in simulations and on IBM superconducting hardware. The work underscores that error-model quality is part of the result, not a detail that can be ignored.
A 2026 benchmark compares specific methods on a specific processor
A 2026 arXiv preprint reports a cross-stack benchmark on a 156-qubit IBM Heron r3 processor. For six tested Ising-observable and size cases, it reports the following mean absolute errors:
| Configuration | Reported mean absolute error |
|---|---|
| IBM raw execution | 0.0883 |
| IBM TREX plus twirling | 0.0807 |
| Q-CTRL | 0.0285 |
| Qedma QESEM | 0.0188 |
For that campaign, the paper reports 211–311 QPU seconds per Estimator job for QESEM and 28 seconds for Q-CTRL. The error figures and QPU times describe the benchmark’s tested configurations; they are not universal rankings or a complete measure of cost. The paper says it did not evaluate monetary price, queueing, classical processing, or end-to-end wall-clock latency.
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What did IBM mean by “110 orders of magnitude”?
In a 2022 blog post, IBM presented an estimated 110-orders-of-magnitude reduction in runtime overhead for a 100-qubit, depth-100 circuit when comparing processor-quality assumptions associated with Hummingbird r2 and Falcon r10. This was a model-based estimate, not a measured customer speedup or an observed end-to-end runtime comparison.
The same IBM post reported γ̄ values of 1.038 for Hummingbird r2, 1.024 for Hummingbird r3, and 1.012 for Falcon r10, measured over the best 10-qubit strings on IBM’s large processors. These reported figures and the modeled runtime comparison concern particular processors and assumptions; neither establishes a general speedup for arbitrary jobs.
How should you judge an error-mitigation result?
A fair comparison should show both what got more accurate and what it cost to get there. When evaluating a claim, look for these details:
- Target result: Which observable or success metric was estimated?
- Workload: What circuit family, width, and depth were tested?
- Conditions: Which processor and noise conditions were used, and how was the noise model obtained?
- Quality: How was accuracy or bias measured, and against what reference?
- Resources: What sampling budget, QPU time, and classical processing were required?
- Evidence type: Is the result a measured hardware outcome, a simulation, or an extrapolation?
- Classical comparison: Does the result beat a strong classical method on the same task, with a comparable accounting of total resources?
There is no single best mitigation method for every workload. IBM has said that selecting optimal settings for large-scale tasks remains an open challenge. Circuit structure, idle time, readout errors, noise-model accuracy, and the cost of additional sampling can all change which approach is useful.
What does this mean for quantum computing performance?
IBM’s work shows ways to reduce noise bias in estimates from selected circuits, alongside real tradeoffs in sampling and runtime. Demonstrations at widths of 100 or 127 qubits are meaningful evidence about the studied experiments, but width alone does not tell a reader whether a computation is accurate, economical, or useful. Those conclusions require workload-specific results and a credible comparison with classical methods.
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