Quantum computing is a model of computation that uses quantum-mechanical phenomena — chiefly superposition and entanglement — to represent and manipulate information using quantum bits, or qubits. Unlike a classical bit, which is definitively 0 or 1, a qubit can exist in a superposition of both states until measured, and groups of entangled qubits exhibit correlations with no classical analogue. These properties allow certain problems to be expressed in ways that, in principle, require fewer operations than the best known classical algorithms.
Quantum computing does not make every computation faster, and it does not replace classical computers. It offers potential advantage on a narrow class of structured problems. Understanding which problems — and recognising current hardware limits — matters for any researcher assessing the field.
Qubits, superposition and entanglement
A qubit is the basic unit of quantum information. Where a classical bit holds one value, a qubit’s state is a combination of the basis states usually written as |0⟩ and |1⟩. This superposition means a register of n qubits can represent a combination of 2n basis states simultaneously. Crucially, you cannot read all of those amplitudes out directly: measurement collapses the qubit to a single classical outcome with a probability set by its amplitude.
Entanglement is a correlation between qubits such that the state of the whole system cannot be described as independent parts. Measuring one entangled qubit constrains the outcomes of others. Quantum algorithms exploit superposition and entanglement together with interference — arranging amplitudes so that wrong answers cancel and correct answers reinforce — to extract useful results from measurement.
How it differs from classical computing
Classical computers are deterministic machines built on bits and Boolean logic gates. Quantum computers use quantum gates that perform reversible, unitary operations on qubit states. The theoretical promise lies in specific algorithms: Shor’s algorithm factors large integers in polynomial time (with implications for cryptography), and Grover’s algorithm offers a quadratic speed-up for unstructured search. Quantum simulation — modelling molecules and materials whose behaviour is itself quantum — is widely regarded as the most natural near-term application.
These advantages are problem-specific and proven only as algorithms; realising them at useful scale depends on hardware that does not yet exist. The distinction between theoretical algorithmic advantage and practical, demonstrated advantage is the single most common source of hype in the field.
The NISQ era
Today’s machines are described as NISQ — Noisy Intermediate-Scale Quantum — devices, a term coined by John Preskill in 2018. They have tens to a few hundred qubits, and those qubits are noisy: they lose coherence quickly and accumulate errors during gate operations. Fault-tolerant quantum computing, which uses quantum error correction to combine many physical qubits into fewer reliable logical qubits, remains a research goal rather than a deployed reality.
| Aspect | NISQ devices (today) | Fault-tolerant (goal) |
|---|---|---|
| Qubit count | Tens to low hundreds (physical) | Many physical per logical qubit |
| Error correction | Limited / partial | Full quantum error correction |
| Coherence | Short; noise dominates depth | Long effective coherence via logical qubits |
| Typical use | Experiments, benchmarks, hybrid algorithms | Shor-scale factoring, large-scale simulation |
Realistic research implications
For most disciplines, the immediate implication of quantum computing is preparatory rather than transformative. Chemistry, materials science and condensed-matter physics have the clearest path to benefit through quantum simulation. Cryptography faces a long-horizon risk: because Shor’s algorithm threatens widely used public-key schemes, standards bodies have begun standardising post-quantum (quantum-resistant) cryptography now, even though a cryptographically relevant quantum computer does not yet exist. Research-data managers should track this as a future migration concern for long-lived encrypted archives.
Quantum methods also intersect with machine learning, though claims of broad quantum-ML advantage remain unproven and contested. Researchers evaluating the field should treat results as research outputs requiring the same reproducibility scrutiny as any computational study, and describe quantum and classical components with consistent standardised terminology so claims can be compared. Sound documentation and metadata practice matters here exactly as it does for data infrastructure generally.
Frequently asked questions
Is quantum computing faster than classical computing?
Only for specific, structured problems where a quantum algorithm exists. For most everyday computing tasks a quantum computer offers no advantage, and classical machines remain superior. Speed-ups are proven for particular algorithms, not for computing in general.
What is the NISQ era?
NISQ stands for Noisy Intermediate-Scale Quantum. It describes today’s devices, which have a modest number of error-prone qubits and lack full error correction. They support experiments and hybrid algorithms but cannot yet run large fault-tolerant computations.
Should researchers worry about quantum computing breaking encryption?
Not imminently, but it is a real long-term concern. Shor’s algorithm could break widely used public-key cryptography once sufficiently powerful, fault-tolerant machines exist. Migration to post-quantum cryptography is being standardised now to protect data that must stay secure for decades.
How does this relate to machine learning?
Quantum machine learning is an active research area, but broad advantages are unproven. For grounding in the classical methods quantum approaches are compared against, see our explainer on machine learning concepts and methods and the companion piece on supervised versus unsupervised learning.
