Quantum computing is a form of computation that uses quantum mechanical states and operations to process information. Conventional computers represent information using classical bits that have values conventionally described as zero or one. Quantum computers use qubits, whose states can exist in coherent superpositions of basis states and can become entangled with other qubits. Quantum algorithms manipulate probability amplitudes through controlled quantum operations and measurements.
This does not mean that a quantum computer simply stores every possible answer simultaneously and reads all of them at once. Measurement produces specific classical outcomes, and useful quantum algorithms must arrange interference so that desired outcomes become more probable while unwanted outcomes are suppressed. Quantum computing is therefore a specialised computational model based on superposition, entanglement, interference, and quantum measurement.
A qubit is the basic unit of quantum information. Its state can be represented as a linear combination of two basis states, conventionally called zero and one. Unlike a classical bit, the state of a qubit can involve complex probability amplitudes associated with both basis states. When measured in the computational basis, the qubit produces either zero or one with probabilities determined by the squared magnitudes of the corresponding amplitudes.
The state cannot be read out as a complete pair of classical values. Measurement changes the information available and generally destroys the particular superposition relative to that measurement basis. Quantum computation therefore involves carefully manipulating amplitudes before measurement. The computational power comes from controlling these amplitudes and their phases rather than from directly observing a hidden collection of simultaneous classical answers.
Entanglement is another essential resource. Two or more qubits can occupy joint quantum states that cannot be represented as independent states assigned separately to each qubit. Measurements of entangled systems can exhibit correlations that have no equivalent description as independent classical random variables under the relevant experimental assumptions. Quantum algorithms can exploit entanglement together with interference to perform transformations that are difficult to reproduce efficiently using straightforward classical methods. However, entanglement by itself does not guarantee computational advantage.
The algorithm, state preparation, measurement strategy, error rates, and classical postprocessing all matter. Some quantum systems can be highly entangled while offering no useful advantage for a particular computational task. Quantum advantage therefore depends on the complete computational problem and implementation rather than simply on the presence of entanglement.
Quantum algorithms are designed to exploit specific structures in computational problems. Shor's algorithm provides a quantum method for integer factorisation and related number theoretic problems with a theoretical complexity advantage over known classical general purpose methods, which has implications for some public key cryptographic systems. Grover's algorithm provides a quadratic speedup for unstructured search under its oracle model. Quantum simulation is another major target because quantum systems can be difficult to simulate efficiently using classical computers as their size increases.
Variational quantum algorithms use parameterised quantum circuits combined with classical optimisation and have been investigated for chemistry and optimisation problems. The actual performance of these algorithms on practical hardware depends strongly on noise, circuit depth, connectivity, error correction, and the availability of efficient classical comparison methods.
Quantum computers can be implemented using different physical technologies. Superconducting circuits use engineered electrical circuits whose relevant states behave quantum mechanically at cryogenic temperatures. Trapped ion systems confine charged atoms using electromagnetic fields and manipulate their internal states with laser or microwave radiation. Neutral atom platforms use arrays of individually controlled atoms, while photonic systems encode information in properties of light. Spin based approaches use quantum states associated with electrons or atomic nuclei. Topological approaches seek to encode information in states with enhanced protection from certain local disturbances, although scalable topological quantum computing remains an active research area. These platforms have different strengths and engineering challenges concerning coherence, gate fidelity, connectivity, measurement, cooling, control, scalability, and manufacturing.
Current quantum computing systems are generally noisy and limited compared with the ideal fault tolerant machines envisioned by quantum information theory. Environmental interactions, imperfect control pulses, material defects, thermal effects, leakage, calibration errors, and measurement imperfections can corrupt quantum states. Quantum error correction addresses these problems by encoding logical information into larger numbers of physical qubits and detecting error syndromes without directly measuring the encoded logical information in a destructive way.
A fault tolerant quantum computer requires physical error rates sufficiently low and a sufficiently suitable architecture to implement reliable logical operations at scale. Error correction introduces substantial overhead, meaning that a useful logical qubit can require many physical qubits depending on the code and hardware. The distinction between physical qubits and logical qubits is therefore fundamental when assessing the practical scale of quantum computing systems.
Quantum computing should not be regarded as a replacement for conventional computing in every application. Classical computers are highly effective for general purpose arithmetic, data processing, operating systems, databases, graphics, and many optimisation and simulation tasks. Quantum processors are expected to be most useful for particular problem structures where quantum algorithms offer a meaningful advantage. Hybrid quantum classical computing is therefore an important model in which a classical computer controls a quantum processor and processes measurement results.
Practical usefulness also depends on total system cost, data loading, communication overhead, error correction, algorithm maturity, and comparison with the best classical algorithms. Claims about quantum speedups must specify the problem, computational model, input assumptions, error requirements, and whether the comparison includes all preprocessing and output costs.
Quantum computing has major implications for cryptography, materials science, chemistry, physics, optimisation, and information theory. Large fault tolerant quantum computers running suitable algorithms could threaten widely used public key systems based on integer factorisation or discrete logarithms, which is why post quantum cryptography develops classical cryptographic algorithms designed to resist known quantum attacks.
Quantum simulation could help investigate molecular electronic structures and materials, potentially supporting research into catalysts, batteries, medicines, and other technologies, although practical advantage for particular applications remains dependent on hardware and algorithmic progress. Quantum sensing can also exploit quantum states for measurements even when the device is not functioning as a general purpose quantum computer. The field therefore encompasses computation, communication, sensing, control engineering, and fundamental physics rather than being limited to one type of processor.
