| quantum computing | |
|---|---|
| Name | Quantum computing |
| Caption | Conceptual quantum circuit |
| Developer | IBM, Google, Rigetti Computing, IonQ, D-Wave, academic laboratories |
| Introduced | mid-1980s |
| Based on | Quantum mechanics, Quantum information science |
| Language | Quantum assembly languages, e.g. OpenQASM |
quantum computing
Quantum computing is a field of study focused on the development of computation devices and algorithms that exploit phenomena of quantum mechanics such as superposition, entanglement and quantum interference to process information. It matters within Quantum Physics because it applies fundamental physical principles to perform tasks that are inefficient or infeasible for classical classical computers, promising new capabilities in simulation, optimization and cryptanalysis.
Quantum computing emerged from theoretical work by researchers including Richard Feynman and David Deutsch who proposed that quantum systems could efficiently simulate other quantum systems. The field intersects experimental physics, theoretical computer science, and engineering; its models—such as the quantum circuit model and the adiabatic quantum computation model—are grounded in the mathematical formalism of Hilbert space and unitary evolution described by the Schrödinger equation. Quantum complexity classes like BQP formalize computational limits under quantum mechanics and contrast with classical classes such as P and NP.
The basic information unit is the qubit, a two-level quantum system realized by physical platforms such as superconducting qubits, trapped ions, quantum dots, or photonic qubits. A qubit can exist in a coherent superposition α|0⟩+β|1⟩ and can become entangled with other qubits, enabling correlations without classical analogues. Quantum information theory extends Shannon information theory with measures like von Neumann entropy and addresses primitives such as quantum teleportation, quantum key distribution (e.g., the BB84 protocol), and no-cloning theorem. Quantum gates are reversible unitary operations; common single- and two-qubit gates include the Hadamard, Pauli gates, CNOT and controlled-phase operations.
Quantum algorithms leverage interference and entanglement to reduce resource costs for certain problems. Prominent algorithms include Shor's algorithm for integer factorization and discrete logarithms, threatening classical public-key cryptosystems based on RSA and Elliptic-curve cryptography. Grover's algorithm provides a quadratic speedup for unstructured search. Simulation algorithms for quantum many-body systems—original motivation by Feynman—enable study of chemistry and materials using methods like the Variational Quantum Eigensolver and Quantum Phase Estimation. Complexity-theoretic results delineate quantum advantage: problems in BQP but not known in P exemplify potential separations; ongoing research relates to Quantum supremacy experiments such as those reported by Google in 2019 and subsequent work by academic groups.
Hardware implementations map qubits to physical degrees of freedom. Superconducting quantum computing (pioneered at institutions like IBM and Google) uses Josephson junction circuits cooled to millikelvin temperatures. Trapped ion quantum computing (commercialized by IonQ and pursued at Joint Quantum Institute) uses laser-manipulated ionic states with long coherence times. Photonic quantum computing employs linear optics and single-photon sources; notable platforms include work by Xanadu. Topological quantum computing—inspired by proposals from Alexei Kitaev—seeks hardware resilient to local errors using non-abelian anyons, pursued experimentally in condensed-matter systems (e.g., Majorana fermion research). Specialized annealing devices from D-Wave implement forms of quantum annealing for optimization.
Quantum systems are fragile due to decoherence and control errors; quantum error correction (QEC) encodes logical qubits into entangled states of many physical qubits. Canonical codes include the Shor code, Steane code, and surface code, the latter favored for two-dimensional architectures with relatively high threshold error rates. Fault-tolerant constructions allow logical gates to be performed while containing errors, relying on techniques like magic state distillation and transversal gates. Threshold theorems establish that below a certain physical error rate, arbitrarily long quantum computations become possible given sufficient overhead, guiding engineering targets for systems developed at labs like NIST and major industrial groups.
Applications span quantum simulation of chemistry and materials for drug discovery and catalysts, optimization problems in logistics and finance, and machine learning subroutines. Quantum algorithms may provide exponential speedups (as in Shor's algorithm) or polynomial/quadratic improvements (as in Grover's algorithm); heuristic or domain-specific quantum advantage has been demonstrated in targeted sampling tasks and noisy-intermediate-scale quantum (NISQ) experiments. Cryptographic implications have motivated developments in post-quantum cryptography standards by organizations such as NIST.
Major challenges include scaling qubit counts while maintaining low error rates, engineering robust control and readout, and reducing overheads of quantum error correction. Cross-disciplinary efforts integrate materials science, cryogenics, control electronics, and software stacks like Qiskit and Cirq for programming and benchmarking. Future directions emphasize fault-tolerant universal quantum computers, hybrid quantum-classical algorithms, and industry–academic collaborations to translate quantum advantage into practical systems. Long-term prospects also consider implications for privacy, national security, and fundamental tests of quantum mechanics, motivating sustained investment by governments and companies including European Commission initiatives and national quantum programs.