Quantum error correction (QEC) protects encoded quantum information by detecting and correcting errors; quantum error mitigation (QEM), often called noise mitigation, uses noisy runs and classical analysis to improve estimates of selected results. QEC trades additional quantum hardware and control for more reliable logical computation. QEM typically trades repeated circuit executions and classical processing for better estimates, without generally making each run fault tolerant. They address errors differently, and can be used together.
How quantum error correction and error mitigation differ
| Comparison | Quantum error correction (QEC) | Quantum error mitigation (QEM) |
|---|---|---|
| Goal | Protect encoded logical information during computation; it is a foundation for fault-tolerant computing. | Improve estimates of selected outputs from noisy executions. |
| How it works | Encodes information across physical qubits, measures error syndromes, then corrects or decodes likely errors. | Repeats or alters executions, characterizes or amplifies noise, and uses classical processing to infer a result. |
| Main resource cost | More physical qubits, gates, measurements, feedback, and decoding. | More circuit executions and samples, calibration, and classical processing. |
| Typical result | A logical computation whose reliability can improve when the code and hardware meet the necessary conditions. | An improved estimate of an observable or other target quantity; not necessarily a fault-tolerant computation. |
| Main limitation | Encoding alone is not enough: code properties, physical noise, and implementation determine whether protection is useful. | Noise assumptions, calibration, sampling, and inference can leave bias or produce unreliable estimates. |
There is no universal cost ratio that makes one method cheaper in every case. The balance depends on the device, code or mitigation method, workload, and reliability required. QEC shifts much of the burden toward quantum hardware and control; QEM shifts it toward sampling and classical inference. A 2023 review surveys QEM techniques and their demonstrated uses and limitations in Reviews of Modern Physics.
How quantum error correction protects information
Quantum states can experience bit-flip and phase errors. Measuring an unknown quantum state directly can destroy the information being computed, so QEC does not simply read out the encoded state to check whether it changed. Instead, a code spreads a logical qubit across multiple physical qubits and measures code checks, called syndromes. Those checks reveal information about errors while preserving the encoded computational information.
A decoder or recovery procedure uses the syndrome to identify a likely error and correct it or account for it. A logical qubit is not literally error-free: protection depends on the code, the physical error rates, and the quality of operations and measurements. IBM’s QEC explainer describes logical information distributed across physical qubits and the measurements used to detect and correct errors.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsHow quantum error mitigation improves estimates
QEM aims to estimate what a less noisy or ideal circuit would have produced. Rather than generally correcting errors during every individual run, mitigation gathers data under selected circuit or noise conditions and processes those results classically. Common approaches include zero-noise extrapolation (ZNE), probabilistic error cancellation, and measurement-error mitigation.
Zero-noise extrapolation
ZNE runs circuits at different noise levels and extrapolates measured values toward a zero-noise result. One way to increase noise is digital gate folding, which inserts equivalent gate sequences while preserving the circuit’s ideal action. IBM’s documented ZNE configuration uses three noise factors by default, with roughly 3× overhead for that configuration; this is not a general cost for all QEM methods. IBM cautions that ZNE “is not guaranteed to produce an unbiased result” and that noise amplification can be inaccurate. The estimate therefore depends on how well the noise is amplified, the extrapolation, calibration, and the number of samples. See IBM Quantum’s error mitigation documentation for its methods and configuration details.
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Readout mitigation and other techniques
Measurement-error mitigation targets errors in the final readout. IBM documents TREX, which twirls measurement outcomes and learns a rescaling term. Pauli twirling randomizes circuits while preserving their ideal action and can make noise more structured, which can support other mitigation strategies. These methods have different assumptions and costs; “mitigation” is a family of approaches rather than one universal cleanup step.
When each approach is useful
QEC: protecting a computation as it runs
QEC is the relevant approach when the goal is to preserve logical information through a computation and ultimately support fault-tolerant operation. It demands sufficient physical qubits, reliable operations, measurement, feedback, and decoding. Whether a particular code provides useful protection depends on the device and implementation, not merely on encoding a logical qubit.
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QEM: improving a noisy device’s measured result
QEM can be useful when a task calls for a better estimate of an observable and repeated noisy executions are available. A 2019 experiment on a superconducting quantum processor applied error mitigation to canonical one- and two-qubit experiments and variational optimization for quantum chemistry and magnetism, reporting improved accuracy without additional hardware modifications. That result demonstrates a method on those experiments; it does not establish a universal advantage across devices or workloads. The primary study appeared in Nature.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the methods can be combined
QEC and QEM are not mutually exclusive. Detection, postselection, or mitigation can be combined with logical error correction to trade hardware resources against sampling and classical work. IBM’s September 15, 2026 perspective describes a continuum from mitigation through error detection and correction to fault tolerance, and argues that mitigation can remain useful alongside logical codes. This is a vendor-authored perspective; any reported performance claims should be understood as IBM-associated results rather than universal evidence. The broader practical point is that the resource balance can change as hardware improves, without making mitigation automatically obsolete.
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