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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallQuantum computing is not known to be impossible, but building a large, useful machine remains an unresolved engineering challenge. Mikhail Dyakonov’s case against it is that precise, coherent control and error correction may not scale from small experiments to useful computations. Later experiments have weakened the claim that error correction is merely theoretical, but they have not shown that general-purpose, fault-tolerant quantum computers are near at hand.
What is the case against quantum computing?
In his 2018 IEEE Spectrum essay, “The Case Against Quantum Computing,” Mikhail Dyakonov focuses on the gap between the mathematical description of a quantum computer and the difficulty of building and operating one. An N-qubit state is described by 2N complex amplitudes. Dyakonov argues that controlling a useful machine therefore poses a problem of precision and scale far beyond what small demonstrations establish.
His argument contrasts quantum systems with conventional digital computers. Classical hardware represents information in discrete bits, and established error-correction techniques can add redundancy to detect and repair bit errors. Quantum states are more delicate: measurement can disturb the state, and physical operations cannot be assumed to be perfectly precise. Dyakonov questions whether the idealized assumptions that make fault-tolerant computing possible can be met in real devices.
He also argues that theory can describe large arrays more readily than engineers can build, calibrate, and operate them. That is a serious feasibility challenge, but it is an argument about engineering scale—not a proof that quantum computation cannot work. The hardware examples and forecasts in a 2018 essay should not be read as measurements of the field today.
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Does a quantum computer have to control every amplitude?
That is a key point of disagreement. In a 2019 response, “The Case for Quantum Computing,” Fred Chong, Ken Brown, and Yongshan Ding argue that Dyakonov treats a quantum computer too much like an analog device that must directly set and maintain every component of its exponentially large state.
Their response describes a modular, digital approach. Quantum error-correction codes encode information across multiple physical qubits. Rather than directly measuring the encoded information, a computer can measure error syndromes: information that helps identify which errors may have occurred while preserving the logical state. The intended result is to detect and correct errors without simply letting control imperfections accumulate across the entire state.
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This answer does not make the engineering problem disappear. Error correction has historically required substantial physical-qubit overhead, and higher physical error rates make that overhead worse. The methods and expectations discussed in the 2019 response describe a strategy and its prospects at that time; they are not evidence that the proposed scale had already been reached.
| Question | Dyakonov’s skeptical emphasis | The technical response and later evidence |
|---|---|---|
| Can error-correction assumptions be achieved in hardware? | Real devices have imperfect preparation, operations, and measurements; idealized threshold assumptions may not hold in practice. | Chong, Brown, and Ding argue that encoded logical operations and syndrome measurements offer a practical digital strategy. A 2025 experiment later demonstrated below-threshold surface-code memory, but not a complete fault-tolerant computer. |
| What happens to resource needs as logical errors fall? | The required control and scale may be far beyond what can be built and operated. | Overhead remains substantial. In its 2025 paper, Google Quantum AI and collaborators extrapolated that a distance-27 logical qubit targeting a 10−6 logical error rate would require 1,457 physical qubits in their setup; this is a projection for that experiment, not a measured universal requirement. |
| Are errors local and manageable? | Real hardware may violate assumptions that make correction tractable. | The 2025 paper identifies rare correlated error bursts and real-time decoding as challenges, including a repetition-code error floor associated with correlated events. |
| Does a logical-memory demonstration establish useful computing? | A few-qubit demonstration does not settle whether large systems can run useful computations. | A memory result is meaningful progress in error correction, but it does not demonstrate long algorithms, commercial advantage, or cryptographic code-breaking. |
What did the 2025 error-correction experiment prove?
Google Quantum AI and collaborators reported a surface-code experiment in Nature, whose version of record appeared on 29 January 2025. The experiment used a 101-qubit, distance-7 logical memory. As code distance increased, its logical error rate improved; the authors reported that increasing distance by two suppressed errors by a factor of 2.14 ± 0.02. They described the system as operating below threshold: in this experiment, increasing the code distance reduced rather than increased the logical error rate.
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The distance-7 logical memory lasted 2.4 ± 0.3 times as long as its best constituent physical qubit. That is important evidence that error correction can produce a logical memory that outperforms its component qubits under the experiment’s conditions. It is not evidence that all error sources have been eliminated or that the same performance follows automatically in other hardware architectures.
A logical memory stores quantum information; a general-purpose fault-tolerant computer must also carry out long sequences of reliable operations at useful scale. The Nature result did not demonstrate commercially useful algorithms or a general-purpose fault-tolerant machine.
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The resource and noise limits still matter
The same paper’s extrapolation for a logical error rate of 10−6 calls for a distance-27 logical qubit using 1,457 physical qubits. That is a projection from the reported experiment, not a measured device or a general estimate for every quantum-computing design. Lower logical error rates and larger computations can require significant additional resources.
The authors also identify real-time decoding—the rapid interpretation of syndrome measurements—as an engineering demand. Rare correlated bursts can be especially troublesome because an error-correction scheme may be less effective when errors occur together rather than independently. The reported repetition-code error floor associated with correlated events illustrates why a below-threshold result does not mean every important noise problem is solved. The paper received an author correction dated 28 April 2026.
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Does this mean quantum computers will never be practical?
No. The evidence supports neither certainty that useful machines will arrive soon nor certainty that they can never be built. The 2025 memory experiment shows progress on a central technical requirement. It does not settle whether engineers can scale error correction, control correlated noise, supply enough physical qubits, and run computations long enough to deliver a useful result.
“Useful” also depends on the task. Foundational research, specialized simulation, commercial advantage, and breaking widely used cryptography are different milestones. Evidence for one does not establish the others.
A 2018 IEEE Spectrum account of the National Academies assessment quoted the committee as saying it was “highly unexpected” that a quantum computer able to compromise RSA-2048 or comparable discrete-log public-key cryptography would be built within the following decade, given the state of the field and recent progress at that time. The committee did not give a specific arrival date for practical machines and said there was no guarantee the challenges would be overcome. That is a dated forecast about a particular cryptographic capability, not a current countdown or a verdict on every possible quantum application.
The same account noted the committee’s view that quantum computing can be valuable for foundational research even if a practical general-purpose machine is never built. The field’s scientific value therefore does not depend solely on whether it reaches commercial or cryptographic milestones.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →How should readers judge claims about quantum computing?
- Look for the demonstrated task. A logical memory is evidence about storing information with error correction; it is not the same achievement as running a long, useful algorithm.
- Separate a measurement from an extrapolation. The 2025 study measured a distance-7 memory and reported its performance. Its distance-27, 1,457-qubit figure is a projection for a specified target in that experiment.
- Ask how the noise behaves. Threshold-based arguments depend on hardware errors being manageable. Correlated events and decoder constraints are among the practical complications identified by the Nature authors.
- Specify what “useful” means. A claim about scientific experiments should not be silently expanded into a claim about commercial advantage or code-breaking.
- Date predictions. The National Academies assessment discussed above was reported in 2018. Scott Aaronson, in a March 2026 interview, described skepticism as having weakened as gate fidelities and error-correction demonstrations improved; that is attributed expert commentary, not experimental evidence.
The strongest skeptical conclusion is that scale, resource costs, and real-world noise could prevent quantum computing from becoming practically useful. The strongest rebuttal is that modular error correction has moved beyond theory into experiments where a logical memory improves with code distance and outlasts its best physical component. Neither position turns the remaining engineering work into a foregone conclusion.
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