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In quantum computing, a qubit is a unit of information analogous to a bit (binary digit) in classical computing, but it is affected by quantum mechanical properties such as superposition and entanglement which allow qubits to be in some ways more powerful than classical bits for some tasks. Qubits are used in quantum circuits and quantum algorithms composed of quantum logic gates to solve computational problems, where they are used for input/output and intermediate computations.
A physical qubit is a physical device that behaves as a two-state quantum system, used as a component of a computer system. [1] [2] A logical qubit is a physical or abstract qubit that performs as specified in a quantum algorithm or quantum circuit [3] subject to unitary transformations, has a long enough coherence time to be usable by quantum logic gates (c.f. propagation delay for classical logic gates). [1] [4] [5]
Since the development of the first quantum computer in 1998, most technologies used to implement qubits face issues of stability, decoherence, [6] [7] fault tolerance [8] [9] and scalability. [6] [9] [10] Because of this, many physical qubits are needed for the purposes of error-correction to produce an entity which behaves logically as a single qubit would in a quantum circuit or algorithm; this is the subject of quantum error correction. [3] [11] Thus, contemporary logical qubits typically consist of many physical qubits to provide stability, error-correction and fault tolerance needed to perform useful computations. [1] [7] [11]
In 2023, Google researchers showed how quantum error correction can improve logical qubit performance by increasing the physical qubit count. [12] These results found that a larger logical qubit (49 physical qubits) had a lower error rate, about 2.9 percent per round of error correction, compared to a rate of about 3.0 percent for the smaller logical qubit (17 physical qubits). [13]
In 2024, IBM researchers created a quantum error correction code 10 times more efficient than previous research, protecting 12 logical qubits for roughly a million cycles of error checks using 288 qubits. [14] [15] The work demonstrates error correction on near-term devices while reducing overhead – the number of physical qubits required to keep errors low. [16]
In 2024, Microsoft and Quantinuum announced experimental results that showed logical qubits could be created with significantly fewer physical qubits. [17] The team used quantum error correction techniques developed by Microsoft and Quantinuum's trapped ion hardware to use 30 physical qubits to form four logical qubits. Scientists used a qubit virtualization system and active syndrome extraction—also called repeated error correction to accomplish this. [18] This work defines how to achieve logical qubits within quantum computation. [19]
1-bit and 2-bit quantum gate operations have been shown to be universal. [20] [21] [22] [23] A quantum algorithm can be instantiated as a quantum circuit. [24] [25]
A logical qubit specifies how a single qubit should behave in a quantum algorithm, subject to quantum logic operations which can be built out of quantum logic gates. However, issues in current technologies preclude single two-state quantum systems, which can be used as physical qubits, from reliably encoding and retaining this information for long enough to be useful. Therefore, current attempts to produce scalable quantum computers require quantum error correction, and multiple (currently many) physical qubits must be used to create a single, error-tolerant logical qubit. Depending on the error-correction scheme used, and the error rates of each physical qubit, a single logical qubit could be formed of up to 1,000 physical qubits. [26]
The approach of topological qubits, which takes advantage of topological effects in quantum mechanics, has been proposed as needing many fewer or even a single physical qubit per logical qubit. [10] Topological qubits rely on a class of particles called anyons which have spin that is neither half-integral (fermions) nor integral (bosons), and therefore obey neither the Fermi–Dirac statistics nor the Bose–Einstein statistics of particle behavior. [27] Anyons exhibit braid symmetry in their world lines, which has desirable properties for the stability of qubits. Notably, anyons must exist in systems constrained to two spatial dimensions or fewer, according to the spin–statistics theorem, which states that in 3 or more spatial dimensions, only fermions and bosons are possible. [27]
A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. Theoretically a large-scale quantum computer could break some widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.
This is a timeline of quantum computing.
In logic circuits, the Toffoli gate, also known as the CCNOT gate (“controlled-controlled-not”), invented by Tommaso Toffoli, is a CNOT gate with two control qubits and one target qubit. That is, the target qubit will be inverted if the first and second qubits are both 1. It is a universal reversible logic gate, which means that any classical reversible circuit can be constructed from Toffoli gates.
Quantum error correction (QEC) is a set of techniques used in quantum computing to protect quantum information from errors due to decoherence and other quantum noise. Quantum error correction is theorised as essential to achieve fault tolerant quantum computing that can reduce the effects of noise on stored quantum information, faulty quantum gates, faulty quantum state preparation, and faulty measurements. Effective quantum error correction would allow quantum computers with low qubit fidelity to execute algorithms of higher complexity or greater circuit depth.
Microsoft Research (MSR) is the research subsidiary of Microsoft. It was created in 1991 by Richard Rashid, Bill Gates and Nathan Myhrvold with the intent to advance state-of-the-art computing and solve difficult world problems through technological innovation in collaboration with academic, government, and industry researchers. The Microsoft Research team has more than 1,000 computer scientists, physicists, engineers, and mathematicians, including Turing Award winners, Fields Medal winners, MacArthur Fellows, and Dijkstra Prize winners.
Quantum networks form an important element of quantum computing and quantum communication systems. Quantum networks facilitate the transmission of information in the form of quantum bits, also called qubits, between physically separated quantum processors. A quantum processor is a machine able to perform quantum circuits on a certain number of qubits. Quantum networks work in a similar way to classical networks. The main difference is that quantum networking, like quantum computing, is better at solving certain problems, such as modeling quantum systems.
A topological quantum computer is a theoretical type of quantum computer proposed by Russian-American physicist Alexei Kitaev in 1997. It utilizes quasiparticles, known as anyons, in two-dimensional systems. These anyons' world lines intertwine to form braids in a three-dimensional spacetime. These braids act as the logic gates of the computer. The primary advantage of using quantum braids over trapped quantum particles is enhanced stability. While small, cumulative perturbations can cause quantum states to decohere and introduce errors in traditional quantum computations, such perturbations do not alter the topological properties of the braids. This stability is akin to the difference between cutting and reattaching a string to form a different braid versus a ball colliding with a wall.
The one-way quantum computer, also known as measurement-based quantum computer (MBQC), is a method of quantum computing that first prepares an entangled resource state, usually a cluster state or graph state, then performs single qubit measurements on it. It is "one-way" because the resource state is destroyed by the measurements.
In quantum computing, the threshold theorem states that a quantum computer with a physical error rate below a certain threshold can, through application of quantum error correction schemes, suppress the logical error rate to arbitrarily low levels. This shows that quantum computers can be made fault-tolerant, as an analogue to von Neumann's threshold theorem for classical computation. This result was proven by the groups of Dorit Aharanov and Michael Ben-Or; Emanuel Knill, Raymond Laflamme, and Wojciech Zurek; and Alexei Kitaev independently. These results built on a paper of Peter Shor, which proved a weaker version of the threshold theorem.
The toric code is a topological quantum error correcting code, and an example of a stabilizer code, defined on a two-dimensional spin lattice. It is the simplest and most well studied of the quantum double models. It is also the simplest example of topological order—Z2 topological order (first studied in the context of Z2 spin liquid in 1991). The toric code can also be considered to be a Z2 lattice gauge theory in a particular limit. It was introduced by Alexei Kitaev.
In quantum many-body physics, topological degeneracy is a phenomenon in which the ground state of a gapped many-body Hamiltonian becomes degenerate in the limit of large system size such that the degeneracy cannot be lifted by any local perturbations.
In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.
Quantum volume is a metric that measures the capabilities and error rates of a quantum computer. It expresses the maximum size of square quantum circuits that can be implemented successfully by the computer. The form of the circuits is independent from the quantum computer architecture, but compiler can transform and optimize it to take advantage of the computer's features. Thus, quantum volumes for different architectures can be compared.
The Eastin–Knill theorem is a no-go theorem that states: "No quantum error correcting code can have a continuous symmetry which acts transversely on physical qubits". In other words, no quantum error correcting code can transversely implement a universal gate set, where a transversal logical gate is one that can be implemented on a logical qubit by the independent action of separate physical gates on corresponding physical qubits.
The current state of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era, characterized by quantum processors containing up to 1,000 qubits which are not advanced enough yet for fault-tolerance or large enough to achieve quantum advantage. These processors, which are sensitive to their environment (noisy) and prone to quantum decoherence, are not yet capable of continuous quantum error correction. This intermediate-scale is defined by the quantum volume, which is based on the moderate number of qubits and gate fidelity. The term NISQ was coined by John Preskill in 2018.
This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.
Zhenghan Wang is a Chinese-American mathematician. He is a principal researcher at Microsoft Station Q, as well as a professor of mathematics at the University of California, Santa Barbara.
Microsoft Azure Quantum is a public cloud-based quantum computing platform developed by Microsoft, that offers quantum hardware, software, and solutions for developers to build quantum applications. It supports variety of quantum hardware architectures from partners including Quantinuum, IonQ, and Atom Computing. To run applications on the cloud platform, Microsoft developed the Q# quantum programming language.
Chetan Nayak is an American physicist and computer scientist specializing in quantum computing. He is a professor at the University of California, Santa Barbara and a technical fellow and distinguished engineer on the Microsoft Azure Quantum hardware team. He joined Microsoft in 2005 and became director and general manager of Quantum Hardware at Microsoft Station Q at Microsoft Research in 2014.