| quantum computation | |
|---|---|
| Name | Quantum computation |
| Domain | Quantum Physics |
| Introduced | 1980s |
| Key people | Paul Benioff, Richard Feynman, David Deutsch |
| Institutions | IBM, Google, Microsoft, D-Wave Systems, Rigetti Computing, University of Oxford, MIT, Harvard University |
quantum computation
Quantum computation is the study and engineering of information processors that exploit principles of quantum mechanics such as superposition, entanglement, and interference to perform computation. It forms a subfield of Quantum Physics and Computer science that redefines fundamental models of information, complexity, and control at microscopic scales. Quantum computation matters because it promises asymptotic speedups for specific problems and a new platform for simulating quantum systems relevant across physics, chemistry, and materials science.
Quantum computation arises directly from formalizing how physical laws constrain computation, a perspective advanced by Rolf Landauer and later by Richard Feynman and David Deutsch. The core elements—qubits, quantum gates, and quantum circuits—are physical degrees of freedom obeying the Schrödinger equation and subject to decoherence through interactions with an environment described by open quantum systems theory. Concepts such as quantum entanglement and the no-cloning theorem link computation to foundational results in quantum information theory. Research spans theoretical analysis (complexity classes like BQP) and experimental realization in platforms developed by industry labs and academic groups at institutions such as IBM, Google, University of California, Berkeley, and Oxford University.
The mathematical basis of quantum computation uses linear algebra over complex Hilbert spaces, operator theory, and probability amplitudes. Fundamental models include the circuit model introduced by David Deutsch and extended via the Quantum Turing machine concept, and alternative frameworks such as adiabatic quantum computation and measurement-based models like cluster state quantum computing (the one-way quantum computer). Formal languages employ quantum gates represented by unitary matrices (e.g., Hadamard gate, Pauli matrices, CNOT gate, Toffoli gate). The theory of quantum channels and completely positive trace-preserving maps formalizes noise and operations, while entropic quantities (von Neumann entropy) and resource theories quantify information and correlations. Complexity theory connects to classes such as BQP, QMA, and relationships with classical classes like NP and P.
Quantum algorithms exploit interference and entanglement to achieve performance beyond classical methods for particular tasks. Representative algorithms include Shor's algorithm for integer factorization, Grover's algorithm for unstructured search, and quantum simulation algorithms for many-body systems (pioneered by Seth Lloyd). Quantum algorithms are often analyzed in terms of query complexity, circuit depth, and gate counts; they motivate cryptographic concerns (impact on RSA and Elliptic-curve cryptography) and inspire post-quantum cryptography research such as lattice-based schemes. Complexity-theoretic milestones include proofs of oracle separations and studies of quantum supremacy/advantage demonstrated in experiments by teams at Google (the Sycamore processor) and contested demonstrations by D-Wave Systems using quantum annealing.
Several physical implementations realize qubits and quantum gates: superconducting circuits (developed by IBM and Google), trapped ions (e.g., work from IonQ and groups at University of Innsbruck), semiconductor spin qubits (pursued by Intel and University of New South Wales), photonic systems (including integrated optics and work by Xanadu), and topological approaches leveraging Majorana fermions pursued by Microsoft. Alternative architectures include quantum annealers (marketed by D-Wave Systems) and hybrid quantum-classical processors integrated with cloud platforms. Engineering considerations involve cryogenic hardware, microwave control, laser systems, and fabrication facilities in university clean rooms and national labs such as Los Alamos National Laboratory and Sandia National Laboratories.
Because quantum states are fragile, quantum error correction and fault-tolerant design are central. Codes such as the Shor code, Steane code, and surface code protect logical qubits via redundancy and syndrome measurement. Threshold theorems quantify error rates below which scalable fault-tolerant computation is possible; practical implementations require high-fidelity gates and error rates informed by experiments at IBM Quantum and other hardware groups. Techniques include dynamical decoupling, quantum control theory, and software-level error mitigation used in near-term noisy intermediate-scale quantum (NISQ) devices popularized in the literature by researchers at MIT and Harvard University.
Quantum computation enables quantum simulation of chemical and condensed-matter systems (of interest to Pfizer and materials research), optimization problems via quantum approximate optimization algorithm (QAOA), and potential advances in machine learning (quantum machine learning). It drives cross-disciplinary collaboration among physicists, chemists, computer scientists, and engineers, and shapes government policy and funding programs such as the National Quantum Initiative (United States) and pan-European initiatives. The field also impacts cryptography, metrology (via quantum-enhanced sensors), and fundamental tests of quantum mechanics.
Key challenges include scaling qubit counts while reducing error rates, developing practical fault-tolerant architectures, and demonstrating clear, widely applicable quantum advantage beyond specialized tasks. Milestones include theoretical proposals (Deutsch, Feynman), experimental achievements in entanglement and gate fidelity, and claims of quantum supremacy by Google and subsequent work by other groups. Future directions emphasize modular quantum networks and quantum repeaters for distributed quantum computing, integration with classical high-performance computing, and exploration of novel materials and topological qubits. Progress will be driven by coordinated efforts across academia, industry, and national laboratories, as exemplified by collaborations among Microsoft, IBM, Google, National Institute of Standards and Technology, and international consortia.
Category:Quantum information science Category:Quantum physics