The current state of quantum computing [1] is referred to as the noisy intermediate-scale quantum (NISQ) era, [2] [3] 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. [4] [5] 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. [6] [2]
According to Microsoft Azure Quantum's scheme, NISQ computation is considered level 1, the lowest of the quantum computing implementation levels. [7] [8]
In October 2023, the 1,000 qubit mark was passed for the first time by Atom Computing's 1,180 qubit quantum processor. [9] However, as of 2024, only two quantum processors have over 1,000 qubits, with sub-1,000 quantum processors still remaining the norm. [10]
NISQ algorithms are quantum algorithms designed for quantum processors in the NISQ era. Common examples are the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA), which use NISQ devices but offload some calculations to classical processors. [2] These algorithms have been successful in quantum chemistry and have potential applications in various fields including physics, materials science, data science, cryptography, biology, and finance. [2] However, due to noise during circuit execution, they often require error mitigation techniques. [11] [5] [12] [13] These methods constitute a way of reducing the effect of noise by running a set of circuits and applying post-processing to the measured data. In contrast to quantum error correction, where errors are continuously detected and corrected during the run of the circuit, error mitigation can only use the outcome of the noisy circuits.
The creation of a computer with tens of thousands of qubits and enough error correction would eventually end the NISQ era. [4] These beyond-NISQ devices would be able to, for example, implement Shor's algorithm for very large numbers and break RSA encryption. [14]
In April 2024, researchers at Microsoft announced a significant reduction in error rates that required only 4 logical qubits, suggesting that quantum computing at scale could be years away instead of decades. [15]
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.
Shor's algorithm is a quantum algorithm for finding the prime factors of an integer. It was developed in 1994 by the American mathematician Peter Shor. It is one of the few known quantum algorithms with compelling potential applications and strong evidence of superpolynomial speedup compared to best known classical (non-quantum) algorithms. On the other hand, factoring numbers of practical significance requires far more qubits than available in the near future. Another concern is that noise in quantum circuits may undermine results, requiring additional qubits for quantum error correction.
This is a timeline of quantum computing.
Quantum Information Science is a field that combines the principles of quantum mechanics with information theory to study the processing, analysis, and transmission of information. It covers both theoretical and experimental aspects of quantum physics, including the limits of what can be achieved with quantum information. The term quantum information theory is sometimes used, but it does not include experimental research and can be confused with a subfield of quantum information science that deals with the processing of quantum information.
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.
Superconducting quantum computing is a branch of solid state physics and quantum computing that implements superconducting electronic circuits using superconducting qubits as artificial atoms, or quantum dots. For superconducting qubits, the two logic states are the ground state and the excited state, denoted respectively. Research in superconducting quantum computing is conducted by companies such as Google, IBM, IMEC, BBN Technologies, Rigetti, and Intel. Many recently developed QPUs use superconducting architecture.
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.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
IBM Quantum Platform is an online platform allowing public and premium access to cloud-based quantum computing services provided by IBM. This includes access to a set of IBM's prototype quantum processors, a set of tutorials on quantum computation, and access to an interactive textbook. As of February 2021, there are over 20 devices on the service, six of which are freely available for the public. This service can be used to run algorithms and experiments, and explore tutorials and simulations around what might be possible with quantum computing.
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.
In quantum computing, a qubit is a unit of information analogous to a bit 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.
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.
Qiskit is an open-source software development kit (SDK) for working with quantum computers at the level of circuits, pulses, and algorithms. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Platform or on simulators on a local computer. It follows the circuit model for universal quantum computation, and can be used for any quantum hardware that follows this model.
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.
In quantum computing, the variational quantum eigensolver (VQE) is a quantum algorithm for quantum chemistry, quantum simulations and optimization problems. It is a hybrid algorithm that uses both classical computers and quantum computers to find the ground state of a given physical system. Given a guess or ansatz, the quantum processor calculates the expectation value of the system with respect to an observable, often the Hamiltonian, and a classical optimizer is used to improve the guess. The algorithm is based on the variational method of quantum mechanics.
This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.
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.
Reliable Quantum Operations Per Second (rQOPS) is a metric that measures the capabilities and error rates of a quantum computer. It combines several key factors to measure how many reliable operations a computer can execute in a single second: logical error rates, clock speed, and number of reliable qubits.