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Quantum error correction reduces the effect of noise by spreading one logical qubit across several physical qubits, checking for error signals without directly measuring the encoded information, and using a decoder to infer how to correct or interpret the result. It does not eliminate faults: it can make logical errors less likely when the hardware, check circuits and decoder are reliable enough for the code being used.
Why quantum computers need error correction
A physical qubit is a hardware element that stores quantum information. It can be affected by imperfect gates and measurements, leakage out of the intended qubit states, and environmental noise. A single fault can change the result of a computation, and simply copying an unknown quantum state is not a way to protect it.
Instead, a quantum error-correcting code encodes information in a logical qubit represented jointly by multiple physical qubits. The computer repeatedly measures selected checks on those qubits. The check results, called a syndrome, reveal information about whether an error pattern has changed without directly measuring the encoded quantum state itself.
How syndrome measurements and decoding reduce errors
Measure checks, not the encoded answer
In a surface-code implementation, data qubits hold the encoded state, while measurement qubits interact with neighboring data qubits to extract parity information. Repeating these checks produces a record of syndromes over time. The pattern can indicate where an error may have occurred, even though it does not reveal the logical state being protected.
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A decoder processes the syndrome record and identifies a likely error pattern. Depending on the experiment, the system may apply a physical correction, or it may use the decoder’s inference to reinterpret the final logical measurement. In memory experiments, correction therefore does not necessarily mean sending an immediate pulse to reverse every individual physical fault.
The protection is probabilistic: errors can still combine into a pattern that changes the logical information, or the syndrome and decoder can fail to identify the problem correctly. Error correction reduces the chance of a logical failure when its checks and decoding work well enough; it does not make every physical qubit flawless.
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Why adding qubits can help—or hurt
A larger code can tolerate more errors before they corrupt the logical qubit. But a larger array also introduces more physical qubits, more operations and more opportunities for faults. The balance depends on the code, the syndrome-measurement circuit, the decoder and the noise conditions.
Below the relevant error-correction threshold, increasing code size can suppress logical errors. Above it, the extra error opportunities may outweigh the protection and make the encoded result less reliable. There is no single threshold that applies to every quantum processor or code.
For one specific example, IBM Research reports a 0.7% threshold for its low-density parity-check approach under the standard circuit-based noise model. That figure is tied to that approach and model; it is not a universal limit for quantum computers.
What Google’s Willow experiment demonstrated
Google Quantum AI and collaborators reported a below-threshold surface-code memory experiment using the Willow architecture. Their paper, “Quantum error correction below the surface code threshold,” was published online on 9 December 2024 and appeared in Nature volume 638, pages 920–926, in the 27 February 2025 issue. The Nature page lists the version of record as 29 January 2025 and records an author correction dated 28 April 2026.
Distance-7 logical memory
The reported distance-7 memory used 49 data qubits, 48 measurement qubits and four additional leakage-removal qubits. The researchers report that each increase of two in code distance reduced logical error per cycle by more than half. They also report that the distance-7 logical lifetime was more than twice that of its best constituent physical qubit.
Long runs and the cost of scaling
The team reports experiments lasting up to 106 error-correction cycles and real-time decoding with a modest accuracy reduction compared with offline decoders. These results show below-threshold scaling in that experimental system; they do not establish that large-scale fault-tolerant computation is already inexpensive or solved.
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The paper’s own projection illustrates the resource challenge: it estimates that reaching a logical error rate of 10−6 would require a distance-27 logical qubit using 1,457 physical qubits. This is the paper’s stated extrapolation, not a general resource estimate for other architectures.
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Residual and correlated errors
Even below threshold, logical failure remains possible. Google identifies correlated bursts as a noise-floor issue in its repetition-code experiments and describes further decoding and scaling challenges. A code’s performance therefore depends not only on average physical error rates, but also on how errors occur and whether the checks and decoder can handle their patterns.
Error correction versus error mitigation
Error correction encodes logical information and uses syndrome data to reduce the likelihood that faults corrupt it. Error mitigation instead estimates or reduces noise effects in measured results without necessarily encoding the computation in a fault-tolerant code. IBM’s explainer notes that applying surface codes on noisy present-day hardware can require an impractically large number of physical qubits per logical qubit.
A demonstrated quantum memory is an important step toward fault-tolerant computing, but it is not the same as a large fault-tolerant processor running useful long algorithms. The memory results establish that logical protection can improve with code size under the demonstrated conditions; the resource overhead and remaining failure modes still matter.
Further reading
For a broader introduction to quantum information and error correction, Cambridge University Press lists Quantum Computation and Quantum Information, 10th Anniversary Edition, by Michael A. Nielsen and Isaac L. Chuang; its contents include a chapter on quantum error correction.
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