Virtual error cancellation (VEC) is a proposed way to improve estimates from logical quantum circuits by using error-correction syndrome records in classical post-processing. It is related to quantum error correction (QEC) because it uses information produced by an error-corrected workflow, but it does not itself correct a quantum state during computation. Zero-noise extrapolation (ZNE) takes a different route: it compares results from executions at deliberately varied noise strengths and estimates what an observable would be at zero noise.
That distinction matters: QEC aims to protect logical information while a computation runs; VEC and ZNE aim to improve estimates derived from computations that have already run. The October 2026 VEC proposal is new and its reported evidence is numerical simulation, not a broad demonstration on quantum hardware.
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What virtual error cancellation means
Virtual error cancellation is the name given by Oles Shtanko, Saswata Roy, Shashwat Kumar, and Zlatko Minev to a logical quantum error-mitigation approach in their preprint, “Measure Now, Mitigate Later: Virtual Error Cancellation for Logical Quantum Circuits.” Submitted to arXiv on October 8, 2026, the work proposes using error-correction syndrome records to improve estimates after a circuit has been executed.
A syndrome is information collected during an error-correction process about inconsistencies that can indicate errors. In a conventional logical-circuit workflow, this information helps a decoder interpret what happened during the computation. VEC takes advantage of the records in a different way: rather than relying only on the decoder used during the experiment, its proposed protocol also uses a more complex decoder in later classical processing to mitigate errors in the final estimate.
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The word “cancellation” can suggest that the method reverses errors in the hardware or restores a measured quantum state. That is not what the proposal describes. The quantum circuit has already run; VEC processes records and output data classically to reduce bias in an estimate. It does not rewind the computation or repair a state after measurement.
Why use two decoders?
The proposal pairs a low-latency decoder used during the experiment with a higher-complexity decoder used offline. This division makes the protocol a “measure now, mitigate later” approach: processing that must fit into the experiment can remain fast, while additional analysis happens after the run. The preprint says that the method can exploit syndrome records without requiring separate noise characterization or modified circuits.
Those are features of the proposed workflow, not a guarantee that every implementation will avoid all calibration, data-processing or hardware constraints. The evidence reported by the authors is numerical, including a simulation of a distance-7 rotated surface code. They report more than three orders of magnitude of suppression compared with the decoded logical error rate for their full-stack dual-decoder algorithm in those numerical simulations. That figure is not a measured reduction in error rates on general-purpose quantum hardware.
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| Approach | Main purpose | When it acts | What it needs |
|---|---|---|---|
| Virtual error cancellation | Mitigate bias in estimates from logical-circuit results using syndrome information | Classical post-processing after execution | Syndrome records and, in the reported protocol, an offline higher-complexity decoder |
| Quantum error correction | Protect logical information during computation | During execution, through encoding, syndrome measurements and correction or decoder action | Redundant physical-qubit encoding and syndrome-processing machinery |
| Zero-noise extrapolation | Estimate an observable in the zero-noise limit | Across a family of executions at deliberately varied noise strengths, followed by classical extrapolation | A noise-scaling procedure, multiple measurements and an extrapolation model |
The table describes different jobs, not competing settings of one universal technique. QEC acts on the handling of logical information during a computation. VEC and ZNE are error-mitigation strategies that process results to improve an estimate. VEC uses syndrome records from an error-corrected workflow; ZNE depends on measurements from noise-scaled executions.
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Quantum error correction protects information during a computation
QEC encodes information in logical qubits using multiple physical qubits. Syndrome measurements check relationships among the physical qubits without simply treating every physical outcome as the logical answer. A correction process or decoder uses syndrome information to identify or manage errors as computation proceeds. The goal is to preserve logical information despite errors affecting constituent physical qubits.
QEC therefore changes the way a computation is represented and run. It requires an encoding, repeated syndrome collection and machinery for processing the resulting information. The resource costs and performance depend on the code, the hardware and the noise. A method that uses syndrome records later does not replace those ingredients: the records must come from a workflow that generates them.
IBM Quantum Learning distinguishes mitigation from correction by describing error correction as encoding information across many physical qubits and checking consistency to preserve information in a logical qubit. In the same conceptual distinction, mitigation changes how results from a noisy computation are processed to estimate an ideal result. QEC is an error-control framework; VEC is a proposed way to get more value from information an error-corrected workflow already records.
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ZNE begins with a different source of information. Instead of relying on syndrome records for the mitigation step, it obtains results at several effective noise strengths and extrapolates an observable toward the zero-noise limit. A scaling method deliberately increases the noise experienced by an execution, and the measured values at the different levels are used to estimate the limiting value.
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Noise can be amplified through circuit modifications or other supported scaling procedures. A 2020 study by Andre He, Benjamin Nachman, Wibe A. de Jong, and Christian W. Bauer examined hardware-agnostic identity-insertion methods and polynomial extrapolation, evaluating them in simulated circuits. The general principle is to create a useful relationship between the scaled noise levels and the measured results, then use a chosen model to extrapolate. The exact implementation need not use identical hardware controls in every setting.
That relationship is also a limitation. The extrapolated estimate is only as useful as the noise scaling and the model’s ability to describe the observed behavior. ZNE involves multiple measurements and classical fitting; it is not just a reinterpretation of one ordinary run. Its sampling demands and reliability depend on the experimental setup and the scaling procedure.
VEC, QEC and ZNE are not interchangeable
VEC versus QEC
The key distinction is timing and purpose. QEC uses encoded information and syndrome-based error handling during computation to protect a logical state. VEC uses syndrome records after execution to mitigate bias in an output estimate. Calling VEC “correction” without qualification can blur the distinction: the proposal does not describe a post-measurement repair of the quantum state.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsVEC nevertheless depends on an important QEC connection. It uses records generated by an error-correction workflow, and its reported protocol uses decoders with different roles. It is best understood as mitigation built around logical-circuit data, rather than as a substitute for QEC.
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VEC versus ZNE
VEC’s described input is syndrome information from logical-circuit execution, with additional offline decoding. ZNE’s input is a set of results from executions at deliberately varied noise strengths. The former is a post-processing method that exploits information available from the error-corrected workflow; the latter changes or scales the experimental conditions and extrapolates across the resulting measurements.
The distinction affects what a workflow must provide. A team considering VEC needs access to suitable syndrome records and the ability to perform the relevant offline decoding. A team considering ZNE needs a viable way to scale noise, gather results at multiple levels and choose an extrapolation model. Neither description implies a universal winner: hardware behavior, sampling cost and the task being estimated matter.
Do not confuse VEC with “virtual error correction”
The similar names are easy to mix up. IBM Quantum Learning notes that probabilistic error cancellation (PEC) is sometimes called “virtual error correction.” That older label is not the same named method as virtual error cancellation in the October 2026 Shtanko, Roy, Kumar and Minev preprint. In particular, the name alone does not establish that VEC is PEC or that the two methods use the same procedure.
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The preprint reports numerical results for a dual-decoder protocol, including a distance-7 rotated surface-code simulation. Its headline quantitative result is more than three orders of magnitude of suppression compared with the decoded logical error rate in those simulations. Read the comparison with its qualifications intact: it is the authors’ result for their algorithm in the stated simulated setting, not a general hardware benchmark or a head-to-head comparison with QEC and ZNE under common conditions.
The work was a very new preprint when submitted in October 2026, rather than a peer-reviewed publication. Its simulation is evidence that the proposed protocol can be evaluated in a logical-circuit setting; it does not establish broad hardware performance, universal benefit, or how it compares numerically with other approaches on the same device. The available sources do not establish a shared numeric benchmark across VEC, QEC and ZNE.
More broadly, error mitigation has limits that depend on the noise model and the platform. A 2022 analysis of fundamental mitigation limits discusses model-dependent performance and sampling and post-processing considerations. A 2023 cross-platform study likewise reports that mitigation benefits depend on the computer. Neither VEC nor ZNE should be presented as fault tolerance, and no one mitigation method can be assumed to win for every circuit or device.
How to choose a method for a workflow
Start with the task. If the priority is to protect logical information while a computation runs, QEC addresses that goal. If the immediate aim is to improve an estimated observable from completed runs, mitigation may be relevant. For a mitigation choice, check the data and control the workflow can actually provide:
- Syndrome access: Does the logical-circuit workflow retain syndrome records suitable for VEC, or are only ordinary circuit outcomes available?
- Noise scaling: Can the experiment run noise-scaled variants while preserving a meaningful relationship between noise level and circuit behavior, as ZNE requires?
- Characterization and model stability: Is the noise model or device characterization needed by the chosen procedure available and stable enough for the task?
- Sampling and processing budget: How many measurements, executions, decoder resources and post-processing steps can the workflow afford?
- Timing requirements: Must decoding or correction happen during the experiment, or can more complex processing wait until afterward?
- Target outcome: Is the goal to preserve a quantum state during computation, or to reduce bias in a final estimate after execution?
These are workflow questions, not a ranking. VEC’s use of recorded syndrome information may be relevant when that information and offline decoder capacity are available. ZNE is relevant when noise-scaled runs and a defensible extrapolation procedure are possible. QEC addresses a different objective and brings its own code-, hardware- and noise-dependent resource requirements.
Bottom line on the distinction
VEC is a newly proposed logical-circuit mitigation method that uses QEC syndrome records in classical post-processing; it is not itself a method for correcting a quantum state during execution. QEC protects logical information as computation proceeds. ZNE estimates a zero-noise result from multiple runs at scaled noise levels. The VEC preprint’s striking suppression figure is limited to numerical simulation, so the practical choice among these methods depends on the goal, available data, experimental controls, sampling budget and noise behavior—not on a universal claim that one approach is best.
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