Quantum computing

Quantum computing is a model of computation that harnesses the principles of quantum mechanics to perform calculations using quantum bits, or qubits, which can represent and manipulate information in fundamentally different ways from the binary bits used in classical computing.

Unlike a classical bit, which is always in one of two definite states (0 or 1), a qubit can exist in a superposition of both states simultaneously. When combined with the phenomena of quantum entanglement and quantum interference, this property allows quantum computers to explore vast solution spaces in parallel and, for certain problem classes, to reach answers exponentially faster than any known classical algorithm.

History and theoretical foundations

The theoretical basis for quantum computing emerged from the intersection of quantum physics and information theory in the early 1980s.

Early proposals

Richard Feynman proposed in 1981 that a computer built from quantum-mechanical components could simulate physical quantum systems more efficiently than classical machines — a task that becomes intractable on classical hardware as system size grows. Around the same time, Paul Benioff described a quantum-mechanical model of a Turing machine.

In 1985, David Deutsch formalised the concept of a universal quantum computer and described the first quantum algorithm, demonstrating that a quantum system could in principle perform any computation a classical computer could, and potentially more.

Landmark algorithms

The field gained urgency in 1994 when mathematician Peter Shor published Shor's algorithm, a quantum algorithm capable of factoring large integers in polynomial time. Because the difficulty of integer factorisation underpins widely used cryptographic systems such as RSA encryption, Shor's result had immediate implications for cybersecurity.

In 1996, computer scientist Lov Grover introduced Grover's algorithm, which provides a quadratic speedup for searching unsorted databases — a more modest but broadly applicable advantage.

Core concepts

Qubits

A qubit is the fundamental unit of quantum information. Physically, qubits can be implemented in several ways, including:

• Superconducting circuits cooled to near absolute zero
• Trapped ions manipulated with laser pulses
• Photons encoding information in polarisation states
• Topological qubits (theoretical and early-experimental)
• Semiconductor spin qubits

The choice of physical implementation involves trade-offs among coherence time, gate fidelity, scalability, and manufacturing complexity.

Superposition

Superposition allows a qubit to occupy a combination of the 0 and 1 states until it is measured. A system of n qubits can represent 2ⁿ states simultaneously, giving quantum processors an inherent parallelism for certain computations.

Entanglement

Quantum entanglement is a correlation between qubits such that the state of one instantly constrains the state of another, regardless of physical separation. Entanglement is a computational resource that enables quantum algorithms to coordinate information across qubits in ways with no classical equivalent.

Quantum interference

Quantum interference is used by quantum algorithms to amplify computational paths leading to correct answers and cancel paths leading to incorrect ones. It is the mechanism by which quantum speedups are actually achieved in practice.

Decoherence and error correction

One of the central engineering challenges in quantum computing is decoherence — the tendency of qubits to lose their quantum properties through interaction with their environment. Decoherence limits the number of operations (the circuit depth) that can be performed before errors accumulate.

Quantum error correction codes, such as the surface code, use redundancy across many physical qubits to protect a smaller number of logical qubits. A fault-tolerant quantum computer capable of running Shor's algorithm at cryptographically relevant scales is estimated to require millions of physical qubits; as of the mid-2020s, leading devices contain thousands.

Hardware approaches and leading platforms

Several technological approaches are being pursued in parallel by research institutions and companies worldwide.

Superconducting quantum processors

Superconducting circuits, operated at millikelvin temperatures, are among the most mature platforms. Google claimed to demonstrate quantum supremacy in 2019 using its 53-qubit Sycamore processor, asserting it completed a specific sampling task in 200 seconds that would take a classical supercomputer thousands of years — a claim subsequently contested by IBM and other researchers who argued classical simulation was more feasible than Google stated.

IBM has pursued an aggressive roadmap of increasing qubit counts and has made quantum processors accessible via cloud services through its IBM Quantum platform.

Trapped-ion systems

Companies such as IonQ and Quantinuum use ions suspended in electromagnetic traps as qubits. Trapped-ion systems typically achieve higher gate fidelities and longer coherence times than superconducting competitors, though they currently face challenges in scaling to large qubit numbers.

Photonic and other approaches

Photonic quantum computing, pursued by companies such as PsiQuantum, encodes qubits in light particles and can operate at room temperature for some components. Neutral atom arrays, explored by firms such as QuEra Computing and Pasqal, have emerged as a rapidly advancing alternative platform.

Applications

Quantum computing is expected to provide advantages across a range of problem domains, though many applications remain theoretical or early-stage.

Cryptography and security

Shor's algorithm threatens current public-key cryptographic infrastructure. In response, the field of post-quantum cryptography has developed classical algorithms believed to be resistant to quantum attacks; the U.S. National Institute of Standards and Technology (NIST) finalised its first post-quantum cryptographic standards in 2024.

Drug discovery and materials science

Simulating molecular and chemical interactions at the quantum level is regarded as one of the most natural applications for quantum hardware. Accurately modelling protein folding, reaction pathways, and novel materials could accelerate pharmaceutical development and the design of catalysts, batteries, and semiconductors.

Optimisation

Problems in logistics, finance, and supply-chain management involve searching enormous solution spaces. Quantum algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) may offer advantages for certain combinatorial optimisation problems, though the practical extent of this advantage on near-term hardware remains an open research question.

Machine learning

Quantum machine learning investigates whether quantum hardware can accelerate training or improve the expressiveness of machine learning models. Results so far are mixed, and demonstrating a genuine advantage over classical methods for realistic datasets remains an active and contested research area.

Current state and outlook

The era of current quantum hardware is often described as the Noisy Intermediate-Scale Quantum (NISQ) era — devices with tens to low thousands of physical qubits that are too noisy for full error correction but large enough for experiments beyond purely academic simulation.

Full fault-tolerant quantum computing — the threshold at which quantum hardware can reliably outperform classical computers on practically important problems — is widely regarded as a medium- to long-term goal. Estimates from researchers and industry analysts vary considerably, with some projecting fault-tolerant machines within a decade and others suggesting the timeline is longer and dependent on breakthroughs in error correction and hardware fabrication.

National governments, including those of the United States, China, members of the European Union, and others, have made substantial public investments in quantum computing research and infrastructure, reflecting both its scientific importance and its perceived strategic significance.

Frequently asked questions

Is quantum computing faster than classical computing for all tasks?

No. Quantum computers provide advantages only for specific problem types where quantum algorithms exist. For most everyday computing tasks — word processing, web browsing, general-purpose logic — classical computers are entirely sufficient and will remain so.

Is quantum computing a threat to current encryption?

In principle, a sufficiently large fault-tolerant quantum computer running Shor's algorithm could break widely used public-key cryptographic schemes such as RSA and elliptic-curve cryptography. Current quantum hardware is nowhere near the scale required, but the threat is taken seriously enough that post-quantum cryptographic standards are already being adopted.

Are quantum computers available today?

Limited-scale quantum processors are accessible via cloud platforms from companies such as IBM, Google, Amazon, and others, allowing researchers to run experiments. These devices are not fault-tolerant and are best suited to research rather than production workloads.

Is quantum supremacy the same as quantum advantage?

Not exactly. Quantum supremacy (sometimes called quantum advantage) refers to demonstrating that a quantum device can solve any specific computational task faster than the best classical computer, regardless of practical utility. Practical quantum advantage refers to outperforming classical computers on a problem of real-world relevance, a milestone that has not yet been unambiguously achieved.

Is topological quantum computing the same as other approaches?

No. Topological quantum computing uses exotic quasiparticles called non-Abelian anyons to encode information in a way that is inherently protected against certain types of errors. It is theoretically attractive but remains at an early experimental stage, with Microsoft being its most prominent industrial proponent.