| quantum computing | |
|---|---|
| Name | Quantum computing |
| Developer | IBM, Google, Microsoft, Rigetti Computing, D-Wave Systems |
| Introduced | mid-20th century |
| Application | Cryptography, simulation, optimization, machine learning |
| Based on | Quantum mechanics |
quantum computing
Quantum computing is a field of study that applies principles of quantum mechanics to perform computation using quantum-mechanical phenomena such as superposition and quantum entanglement. It matters within Quantum Physics because it leverages foundational physical laws to offer potentially exponential speedups for certain problems compared with classical classical computers, influencing technology, national security, and economic competitiveness.
The concept of computation using quantum phenomena was anticipated by thinkers such as Paul Benioff, who described a quantum mechanical model of the Turing machine in 1980, and formalized by Richard Feynman and David Deutsch in the 1980s. Deutsch proposed the quantum Turing machine and universal quantum gates, while Peter Shor's 1994 algorithm for integer factoring and Lov Grover's 1996 search algorithm demonstrated practical advantages. Early experimental demonstrations were performed in systems developed at institutions including IBM Research, Bell Labs, Los Alamos National Laboratory, and universities such as Massachusetts Institute of Technology and Caltech. Government programs such as those run by the NASA and the NIST accelerated development, and industrial investments by Intel, Honeywell, and venture-backed startups shaped commercialization.
Quantum computing rests on postulates of quantum mechanics such as state vectors in Hilbert space, unitary evolution by the Schrödinger equation, and measurement collapse. Key formal tools include Dirac notation, density matrix formalism, and the theory of open quantum systems to describe decoherence. Foundational concepts like Bell's theorem and No-cloning theorem impose constraints and enable cryptographic primitives; the Church–Turing thesis has a quantum analogue in the quantum Church–Turing thesis. Theoretical frameworks are developed by researchers in theoretical physics and applied mathematics, and propagated at conferences such as the QIP conference and by journals like Physical Review Letters and Nature Physics.
The basic unit of quantum information is the qubit, implemented across diverse physical platforms: superconducting qubits (transmons) developed by Yale University and companies like IBM and Google, trapped ions advanced at University of Innsbruck and IonQ, topological qubit proposals inspired by Alexei Kitaev and pursued by Microsoft Research, spin qubit implementations in silicon championed by Intel and UNSW, and photonic systems from groups such as Xanadu and the University of Bristol. Specialized devices such as quantum annealers by D-Wave Systems exploit quantum tunneling for optimization. Each platform balances coherence times, gate fidelities, connectivity, and manufacturability, with metrics standardized by laboratories like NIST.
Quantum algorithms exploit interference and entanglement to reduce complexity for some tasks. Landmark algorithms include Shor's algorithm for integer factoring and discrete logarithms, Grover's algorithm for unstructured search, and quantum simulation algorithms inspired by Feynman for modeling many-body quantum systems. Complexity classes such as BQP (bounded-error quantum polynomial time) and their relation to classical classes like NP and P guide theoretical expectations. Work by researchers including Lov Grover, Peter Shor, Ethan Bernstein, Umesh Vazirani, and Andrew Yao established formal limits. More recent algorithmic advances include quantum algorithms for linear systems (the HHL algorithm), Hamiltonian simulation, quantum machine learning, and variational approaches such as the VQE and QAOA.
Decoherence and noise require quantum error correction to achieve scalable computation. Codes such as the Shor code, Steane code, and surface code (developed from work by Alexei Kitaev and others) protect logical qubits using redundant physical qubits. Fault-tolerant architectures use transversal gates, magic state distillation, and concatenated codes to implement universal quantum gates under noisy conditions. Threshold theorems quantify error rates below which reliable computation is possible; experimental efforts at Google, IBM, Microsoft Research, and national labs aim to reduce gate error and build logical qubits in platforms like superconducting circuits and trapped ions.
Architectural choices address qubit connectivity, control electronics, cryogenics, and fabrication. Scalable proposals include modular networks linked by quantum teleportation and quantum networking using quantum repeaters, cryogenic multiplexing for superconducting processors, and photonic interconnects for distributed systems. Large-scale efforts include national initiatives such as the U.S. National Quantum Initiative, the European Quantum Flagship, and programs in China and Japan. Commercial roadmaps from IBM Quantum and development platforms like Google Quantum AI publish milestone roadmaps toward systems with hundreds to millions of qubits, while ecosystem partners include foundries, cryogenic vendors, and accelerator design groups.
Potential applications span cryptography (threatening RSA and ECC), quantum simulation for materials science and drug discovery, optimization for logistics and finance, and enhanced sensors for metrology. Post-quantum cryptography initiatives by NIST and standards bodies aim to mitigate cryptographic risks. Quantum advantage demonstrated in limited tasks by Google's Sycamore experiment prompted national policy responses emphasizing resilience, workforce development, and industrial partnerships. The strategic significance has led to increased funding, export controls, and collaboration among universities like Harvard University, University of California, Berkeley, and national labs for maintaining technological leadership and national cohesion.
Category:Quantum information science Category:Quantum mechanics