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Quantum computers use qubits and quantum effects such as interference and entanglement to process information in ways classical computers cannot directly reproduce. They are specialized machines, not faster replacements for ordinary computers: fragile quantum states and imperfect operations restrict what today’s devices can reliably do. Error correction is the route toward larger, dependable computations, but it requires substantial hardware and classical processing overhead.
How does quantum computing work?
A classical computer stores information in bits, each with a value of 0 or 1. A quantum computer stores quantum information in qubits. A qubit can be prepared in a superposition of the basis states 0 and 1; this is a quantum state, not a hidden list of answers that the computer evaluates simultaneously.
When a qubit is measured, the result is a classical value. Measurement limits what can be learned from the quantum state, so a quantum algorithm must arrange its operations before measurement. Interference changes the likelihood of different outcomes, while entanglement creates correlations between qubits that have no direct classical equivalent. Algorithms use these effects to make useful results more likely for particular tasks. IBM Quantum Learning explains quantum technology and performance.
Physical and logical qubits
A physical qubit is a hardware element that holds and manipulates quantum information. It is vulnerable to disturbances and faulty operations. A logical qubit is information encoded across multiple physical qubits using an error-correcting code. The extra physical qubits provide a way to detect and address errors while protecting the logical information; they do not simply make each underlying component error-free.
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Why are quantum computers difficult to scale?
Quantum states are sensitive to environmental disturbance and imperfect operations. Decoherence and other noise can corrupt a computation, limiting how many circuit operations a noisy device can reliably execute. Errors can accumulate as a processor grows, so increasing the physical-qubit count alone does not ensure a more capable or dependable machine.
Performance depends on more than scale. IBM Quantum Learning identifies scale, quality, and speed as useful dimensions: how many qubits are available for a workload; how reliably the hardware can perform operations; and how quickly it can execute circuits. The circuit’s size and demands, as well as the task being attempted, also matter. As IBM puts it, “Today’s quantum computers are not yet fully fault tolerant, so understanding their performance requires consideration of multiple factors beyond qubit count alone.” Read IBM Quantum Learning’s performance overview.
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What is quantum error correction?
Quantum error correction protects logical information by encoding it redundantly across physical qubits. It cannot copy an unknown quantum state in the ordinary classical sense. Instead, the system measures selected properties that reveal evidence of errors without directly measuring the encoded state. The resulting information is called an error syndrome.
- Encode: Distribute logical information across physical qubits according to a code.
- Extract a syndrome: Measure selected checks that reveal likely errors without directly reading the logical state.
- Decode: Use classical computation to interpret the syndrome and infer a correction.
- Correct and repeat: Apply the inferred correction, then repeat syndrome extraction as computation continues.
Every stage can itself be faulty. A practical code and implementation must control those faults so errors do not spread or accumulate faster than the system can detect and correct them. The code, hardware quality, qubit connectivity, repeated measurements, decoder speed, logical operations, and physical-qubit overhead all affect whether protection can support useful computation.
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Error correction is not the same as error mitigation
Error correction uses encoded logical qubits and repeated syndrome measurements to detect and correct errors as part of fault-tolerant computation. Error mitigation uses methods to reduce the impact of noise on results without fully correcting errors through that encoded, repeated process. Error suppression aims to reduce error at its source or limit its effects. These approaches address reliability differently and can coexist as quantum hardware develops; mitigation does not by itself establish fault tolerance.
What does fault-tolerant quantum computing mean?
Fault tolerance is the broader design discipline for carrying out logical computation despite imperfect physical components. It requires operations, including logical gates, to be implemented in ways that prevent local errors from spreading uncontrollably. As Robert Davis, Olivia Lanes, and John Watrous define it in IBM’s May 30, 2025 explainer, “A fault-tolerant quantum computer is a quantum computer designed to operate correctly even in the presence of errors.” IBM’s fault-tolerant quantum computing explainer.
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An error-corrected memory alone is not proof of scalable fault-tolerant computing: reliable logical operations and the supporting architecture matter too. When evaluating a claimed advance, check whether logical error rates improve as code size increases, how many physical qubits are used per logical qubit, how many correction cycles were completed, which operations were demonstrated, and whether the result covers memory or computation.
Why the first error-correcting code is a teaching milestone
The nine-qubit Shor code encodes one logical qubit using nine physical qubits. IBM describes it as the first quantum error-correcting code, but also notes that it is not a practical code and tolerates only a minuscule error rate. It illustrates the principle of encoding information to detect and correct errors; it should not be treated as a blueprint for large-scale quantum hardware. IBM Quantum Learning’s introduction to correcting quantum errors.
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What are quantum computers used for?
Current noisy quantum devices are used to explore algorithms and run carefully scoped experiments, often in hybrid workflows that also use classical high-performance computing. Such work can show research progress on a particular workload, especially when results are checked against classical methods or use error mitigation. It does not establish that quantum computers broadly outperform classical systems.
Scientific problems are among the most grounded prospective targets for future fault-tolerant machines:
- Quantum chemistry: Researchers are investigating whether quantum computation could help model chemical systems.
- Materials science: Quantum methods may help study materials whose behavior is difficult to capture with classical approaches.
- High-energy and nuclear physics: Future systems may contribute to computational problems in these fields.
These are research opportunities, not established routine commercial breakthroughs. They depend on advances in algorithms, systems, and hardware. Quantum computers are specialized machines; they are not generally faster for every task and are not replacements for laptops or servers. Claims about optimization, drug discovery, machine learning, or codebreaking should be judged by the specific demonstrated workload and its limits, rather than treated as already solved commercial applications. The Department of Energy’s overview of quantum computing for scientific discovery.
How to judge a quantum-computing breakthrough
A qubit total or a roadmap target is not enough to establish practical advantage. For a reported result, ask:
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- What scale was available? Count qubits usable for that workload, not only the processor’s headline total.
- How reliable were the operations? Look for circuit demands, error rates, and the quality of results.
- What was the speed? Consider circuit execution throughput and total time, including classical processing where relevant.
- Was error correction demonstrated? Distinguish physical-qubit experiments, error mitigation, protected logical memory, and fault-tolerant computation.
- Was there a classical comparison? A claimed advantage needs a clearly defined comparison on the same problem and meaningful conditions.
Agency targets are goals, not delivery records. For example, the National Quantum Initiative’s December 2024 supplement to the President’s FY 2025 Budget describes IARPA’s final goal as a 95% or higher average success rate for teleporting cardinal logical states in a modular, fault-tolerant architecture. That figure is a program goal in the report, not an achieved result. Read the National Quantum Initiative’s FY 2025 budget supplement.
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