Quantum Computing Language
Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.
With quantum processor based systems, quantum programming languages help express quantum algorithms using high-level constructs. The field is deeply rooted in the open-source philosophy and as a result most of the quantum software discussed in this article is freely available as open-source software.
Quantum computers, such as those based on the KLM protocol, a linear optical quantum computing (LOQC) model, use quantum algorithms (circuits) implemented with electronics, integrated circuits, instrumentation, sensors, and/or by other physical means.
Other circuits designed for experimentation related to quantum systems can be instrumentation and sensor based.
IN THIS ARTICLE ........
Meet Twist: MIT’s Quantum Programming Language
Q# and the Quantum Development Kit
The Quantum Programming Language Q#
Quantum Development Workflow With The QDK
Quantum Software Development Kits
Quantum Languages
Quantum programming languages are essential to translate ideas into instructions that can be executed by a quantum computer. Not only are they crucial to the programming of quantum computers at scale but also they can facilitate the discovery and development of quantum algorithms even before hardware exists that is capable of executing them.
Quantum programming languages are used for controlling existing physical devices, for estimating the execution costs of quantum algorithms on future devices, for teaching quantum computing concepts, or for verifying quantum algorithms and their implementations. They are used by newcomers and seasoned practitioners, researchers and developers working on the next ground-breaking discovery or applying known concepts to real-world problems. This variety in purpose and target audiences is reflected in the design and ecosystem of the existing quantum programming languages, depending on which factors a language prioritizes.
Key points
· Quantum computing fundamentally differs from other means of computing, requiring the development of domain-specific programming languages and compilation techniques.
· Quantum programming languages today cater to a variety of purposes and target audiences ranging from newcomers to seasoned practitioners, and which factors are prioritized substantially impacts the design of the language, the ecosystem and the community around it.
· Programming languages and software tools facilitate the discovery, advancement and development of quantum applications by enabling the verification, resources estimation, program analysis and visualization of quantum applications; they are essential for understanding and analysing both large-scale applications and algorithms for near-term hardware and suitable circuit compilation methods.
· The requirements for software tools for near-term devices and applications differ substantially from those geared towards scalable, fault-tolerant quantum computing.
· The field of quantum programming languages and compilers is still nascent, and current research and implementation efforts are focused on low-level circuit optimizations, concentrating on the purely quantum pieces rather than on optimizations that act across the entire program structure.
· Emulating and accelerating a similar progression to that followed by conventional computing over the past 50 years requires a collaborative effort across disciplines to take full advantage of the acquired knowledge.
Bettina Heim, Mathias Soeken, Sarah Marshall, Chris Granade, Martin Roetteler, Alan Geller, Matthias Troyer & Krysta Svore
Meet Twist: MIT’s Quantum Programming Language
Quantum computing. Unlike traditional computers that use bits, quantum computers use qubits to encode information as zeros or ones, or both at the same time. Coupled with a cocktail of forces from quantum physics, these fridge-sized machines can process a whole lot of information – but they’re far from flawless. Just like our regular computers, we need to have the right programming languages to properly compute on quantum computers.
Programming quantum computers requires awareness of something called “entanglement”, a computational multiplier for qubits of sorts, which translates to a lot of power. When two qubits are entangled, actions on one qubit can change the value of the other even when they are physically separated, giving rise to Einstein’s characterization of “spooky action at a distance.” But that potency is equal parts a source of weakness. When programming, discarding one qubit without being mindful of its entanglement with another qubit can destroy the data stored in the other, jeopardizing the correctness of the program.
Scientists from MIT’s Computer Science and Artificial Intelligence (CSAIL) aimed to do some unraveling by creating their own programming language for quantum computing called “Twist.” Twist can describe and verify which pieces of data are entangled in a quantum program, through a language a classical programmer can understand. The language uses a concept called purity, which enforces the absence of entanglement and results in more intuitive programs, with ideally fewer bugs. For example, a programmer can use Twist to say that the temporary data generated as garbage by a program is not entangled with the program’s answer, making it safe to throw away.
While the nascent field can feel a little flashy and futuristic, with images of mammoth wiry gold machines coming to mind, quantum computers have potential for computational breakthroughs in classically unsolvable tasks, like cryptographic and communication protocols, search, and computational physics and chemistry. One of the key challenges in computational sciences is dealing with the complexity of the problem and the amount of computation needed. Whereas a classical digital computer would need a very large exponential number of bits to be able to process such a simulation, a quantum computer could do it, potentially, using a very small number of qubits – if the right programs are there.
“Our language Twist allows a developer to write safer quantum programs by explicitly stating when a qubit must not be entangled with another,” said MIT PhD student Charles Yuan, the lead author on a paper about Twist. “Because understanding quantum programs requires understanding entanglement, we hope that Twist paves the way to languages that make the unique challenges of quantum computing more accessible to programmers.”
Untangling quantum entanglement
The scientists designed Twist to be expressive enough to write out programs for well-known quantum algorithms and identify bugs in their implementations. To evaluate Twist's design, they modified the programs to introduce some kind of bug that would be relatively subtle for a human programmer to detect, and showed that Twist could automatically identify the bugs and reject the programs.
They also measured how well the programs performed in practice in terms of runtime, which had less than four percent overhead over existing quantum programming techniques.
For those wary of quantum’s “seedy” reputation in its potential to break encryption systems, it’s still not very well known to what extent quantum computers will actually be able to reach their performance promises in practice. “There's a lot of research that's going on in post-quantum cryptography, which exists because even quantum computing is not all-powerful. So far, there's a very specific set of applications in which people have developed algorithms and techniques where a quantum computer can outperform classical computers.”
An important next step is using Twist to create higher-level quantum programming languages. Most quantum programming languages today still resemble assembly language, stringing together low-level operations, without mindfulness towards things like data types and functions, and what’s typical in classical software engineering.
“Quantum computers are error-prone and difficult to program. By introducing and reasoning about the ‘purity’ of program code, Twist takes a big step towards making quantum programming easier by guaranteeing that the quantum bits in a pure piece of code cannot be altered by bits not in that code,” says Fred Chong, the Seymour Goodman Professor of Computer Science at the University of Chicago and Chief Scientist at Super.tech.
The work was supported in part by the MIT-IBM Watson AI Lab, the National Science Foundation, and the Office of Naval Research.
Yuan wrote the paper alongside MIT PhD student Chris McNally of the Research Laboratory of Electronics and MIT professor Michael Carbin.
Rachel Gordon csail.mit.edu Quantum Computing
Q# and the Quantum Development Kit
The Quantum Development Kit (QDK) is the SDK required to interface with the Azure Quantum service. It is built-in to the Azure Quantum portal, where you can develop programs using the free hosted Jupyter Notebooks. You can also install the QDK locally on your computer to use your own local development environment and work both online and offline with the Azure Quantum service. With the Quantum Development Kit, you can build programs that run on quantum hardware in Azure Quantum.
The QDK includes the quantum programming language Q#, a high-level, open-source programming language that allows you to focus your work at the algorithm and application level to create quantum programs.
The QDK offers support for Q#, but also for Qiskit, and Cirq for quantum computing, so if you are already working in other development languages, you can also run your circuits on Azure Quantum.
The QDK contains components that can be used standalone, independently from the Azure Quantum service:
- Q# language and quantum libraries, all open-source.
- Quantum simulators that simulate current and future quantum machines, so that you can run and debug your quantum algorithms written in Q#.
- Extensions for Visual Studio 2022 and Visual Studio Code, and integration with Jupyter Notebooks.
Get started with Q# and the Quantum Development Kit
To jump right in, you can explore Q# in the Azure Quantum portal with no installation required. For more information, see Get started with Q# and an Azure Quantum notebook.see Setup the Quantum Development Kit.
Tip
If you want to accelerate your quantum computing journey, check out the Copilot in Azure Quantum, a unique feature of the Azure Quantum website. With the Copilot you can run your Q# programs, generate new Q# code from your prompts, and ask any questions about quantum computing.
The Quantum Programming Language Q#
Q# is an open-source, high-level, programming language for developing and running quantum algorithms. Its part of the Quantum Development Kit (QDK) and it's designed to be hardware agnostic, scale to the full range of quantum applications, and to optimize execution. For more information on the Q# language development project, see the Q# and core libraries design repository.
As a programming language, Q# draws familiar elements from Python, C#, and F#, and supports a basic procedural model for writing programs with loops, if/then statements, and common data types. It also introduces new quantum-specific data structures and operations, such as repeat-until-success and adaptive phase estimation, which allow the integration of quantum and classical computations. For example, the flow control of a classical program can be based on the outcome of a quantum measurement.
Integration with quantum and classical computation
Q# is a standalone language offering a high level of abstraction. There is no notion of a quantum state or a circuit; instead, Q# implements programs in terms of statements and expressions, much like classical programming languages. Distinct quantum capabilities, such as support for functors and control-flow constructs, facilitate expressing, for example, phase estimation and quantum chemistry algorithms.
The Q# language supports the integration of rich classical and quantum computing. This allows clean expression of adaptive algorithms such as the random walk phase estimation operation that are difficult to express directly in the circuit model of a fixed sequence of quantum gates.
Q# supports general classical flow control during the execution of an algorithm. In particular, classical flow control is based on quantum measurement outcomes, which makes it much easier to write things that depend on intermediate measurements. For instance, the loop required for probabilistic algorithms such as Grover search can easily be expressed in Q#, rather than having to return to the classical driver to test whether the result satisfies the oracle and rerunning if not.
Qubits as opaque references
In Q#, qubits are a resource that are requested from the runtime when needed and returned when no longer in use. This is similar to the way that classical languages deal with heap memory.
In Q# qubits are modeled as opaque data types that represent a reference to a specific two-state quantum system, whether physical or logical (error-corrected), on which quantum operations may be performed. This is an operational view of qubits - that is, qubits are defined by what you can do to them.
The mapping from a qubit variable in a program to an actual logical or physical qubit is decided by the runtime, and that mapping may be deferred until after the topology and other details of the target device is known. The runtime is responsible for determining a mapping that allows the algorithm to execute, including any qubit state transfer and remapping required during execution.
The representation used in Q# has the interesting implication that all of the actual quantum computing is done by side effect. There is no way to directly interact with the quantum state of the computer; it has no software representation at all. Instead, you perform operations on qubit entities that have the side effect of modifying the quantum state. Effectively, the quantum state of the computer is an opaque global variable that is inaccessible except through a small set of accessor primitives (measurements) — and even these accessors have side effects on the quantum state, and so are really “mutators with results” rather than true accessors.
Respect the laws of physics
Quantum programs should be required to respect the laws of physics. For example, copying the state of a qubit or directly accessing the qubit state are not possible in Q#.
Therefore, Q# has no ability to introspect into the state of a qubit or other properties of quantum mechanics directly, which guarantees that a Q# program can be physically executed on any quantum computer. Instead, a Q# program has the ability to call operations and functions, such as DumpOperation operation, to extract classical information from a qubit, that allow validation and state examination to facilitate debugging with a simulator.
Hardware agnostic
Q# is hardware agnostic, meaning that it provides the means to express and leverage powerful quantum computing concepts independently of how hardware evolves in the future. To be useable across a wide range of applications, Q# allows you to build reusable components and layers of abstractions. To achieve performance with growing quantum hardware size, the Q# quantum programming language ensures the scalability of both applications and development effort. Even though the full complexity of such computations requires further hardware development, Q# programs can be targeted to run on various quantum hardware backends in Azure Quantum.
Efficient execution
The Q# language is focused on expressing information to optimize the execution of quantum components, independent of the context within which they are invoked. Q# allows the developer to communicate their knowledge about a computation so that the compiler can make an informed decision regarding how to translate it into instructions, leveraging information about the end-to-end application that is not available to the developer.
To learn more about the QDK features and the general pieces that fit within a Q# program, see the Q# quantum programming language user guide.
Quantum Development Workflow With The QDK
When you compile and run a quantum program, the QDK creates an instance of the quantum simulator and passes the Q# code to it. The simulator uses the Q# code to create qubits (simulations of quantum particles) and apply transformations to modify their state. The results of the quantum operations in the simulator are then returned to the program. Isolating the Q# code in the simulator ensures that the algorithms follow the laws of quantum physics and can run correctly on quantum computers.
Everything you need to write and run Q# programs, including the Q# compiler, the Q# libraries, and the quantum simulators, is pre-deployed in the hosted Jupyter Notebooks in the Azure portal. The QDK can also be installed and run from your local computer, so you can use your preferred IDE and language locally and submit jobs to quantum hardware or cloud-based simulators on Azure Quantum, or work with local simulators. For more information, see configure your quantum development environment.
The following diagram shows the stages through which a quantum program goes from idea to complete implementation on Azure Quantum, and the tools offered by the QDK for each stage.
Note
You use the same Q# code for all steps of the workflow. In the short term you might have to tweak some portions of the code to account for the current hardware limitations. But in the long run you’ll be able to switch between various simulators and hardware providers without any code modifications.
Write your quantum program
A Q# program can compile into a standalone application, run in a Jupyter Notebook, or be called by a host program that is written either in Python or a .NET language.
If you want to start practicing and writing your Q# programs without installing additional software, you can use the hosted Jupyter Notebooks available in your Azure Quantum workspace in the Azure portal. The sample gallery contains a collection of annotated notebook samples - select the sample you want to explore and run it on cloud-based simulators or real quantum computers.
If you prefer a local development environment, you can create your Q# program using Jupyter Notebooks with the IQ# kernel, or the QDK extensions for Visual Studio Code and Visual Studio 2022, and then run them against quantum hardware, or cloud-based or local simulators.
Whichever environment you prefer, you can follow the Q# tutorials and start writing quantum programs to explore quantum concepts such as superposition, entanglement, Grover's quantum algorithm, and other quantum phenomena.
If you don't have an Azure account, and you want to try Q# without installing the QDK locally, you can use Binder to run the Q# samples in Jupyter Notebooks online.
Explore domain-specific libraries
The Q# libraries will help you keep your code high level, enabling you to run complex quantum operations without having to design low-level operation sequences. New Q# projects automatically include the Q# standard library, which provides a set of essential and very useful functions and operations that can be used when writing quantum programs in Q#.
In addition to the standard library, the QDK includes a quantum chemistry library for simulating quantum dynamics and electronic structure problems on quantum computers, a quantum machine learning library which provides an implementation of the sequential classifiers that take advantage of quantum computing to run hybrid quantum/classical machine learning experiments, and a quantum numeric library which provides support for a wide range of numerics functionality.
Run programs in simulators
Once you’ve written your program, the QDK offers a set of quantum simulators - classical programs that simulate the behavior of a quantum system - so that you can run a small instance of your program and see what it does without actual hardware access. You can run your quantum programs on a full-state quantum simulator, a limited-scope Toffoli simulator, a sparse simulator for systems with larger number of qubits, and even use the noise simulator for simulating the behavior of Q# programs under the influence of noisy environments.
See the full list of quantum simulators.
Estimate resources
Before running on quantum hardware, you’ll need to figure out whether your program can run on existing hardware, and how many resources it will consume. You can use the trace simulator to estimate how many qubits and quantum gates you need and debug classical code that is part of your quantum program.
You can also submit your quantum program to the Azure Quantum Resource Estimator target in Azure portal. The Azure Quantum Resource Estimator computes post-layout physical resource estimation by taking assumptions about qubit parameters, quantum error correction codes, and an error budget into account. It is free of charge and requires an Azure account.
Submit jobs to the Azure Quantum service
Azure Quantum is the cloud quantum computing service of Azure, with a diverse set of quantum solutions and technologies. Azure Quantum ensures an open, flexible, and future-proofed path to quantum computing that allows you to run your program on quantum hardware. You can run your Qiskit, Cirq, and Q# programs on multiple quantum systems. See Quantum computing providers for the current list of supported hardware providers.
Tip
First-time users automatically get free $500 (USD)Azure Quantum Credits for use with each participating quantum hardware provider. If you have consumed all the credits and you need more, you can apply to the Azure Quantum Credits program.
Once you’ve created an Azure Quantum workspace, you can submit your Q# programs (also known as jobs) to Azure Quantum through your preferred development environment, both locally and online. For more information, see how to submit Q# jobs. You can also run and submit quantum circuits written in Qiskit or Cirq.
Azure Quantum offers some of the most compelling and diverse quantum resources available today from industry leaders. With Azure Quantum and the QDK you can write quantum computing programs, and submit them to Azure Quantum to run on quantum hardware.
If you want to learn more, the Quantum Katas provide a good introduction to quantum computing concepts such as common quantum operations and how to manipulate qubits.
Learn.microsoft.com what-is-qsharp-and-qdk
Quantum Instruction Sets
Quantum instruction sets are used to turn higher level algorithms into physical instructions that can be executed on quantum processors. Sometimes these instructions are specific to a given hardware platform, e.g. ion traps or superconducting qubits.
cQASM
cQASM, 3 also known as common QASM, is a hardware-agnostic quantum assembly language which guarantees the interoperability between all the quantum compilation and simulation tools. It was introduced by the QCA Lab at TUDelft.
Quil
Quil is an instruction set architecture for quantum computing that first introduced a shared quantum/classical memory model. It was introduced by Robert Smith, Michael Curtis, and William Zeng in A Practical Quantum Instruction Set Architecture. 4 Many quantum algorithms (including quantum teleportation, quantum error correction, simulation, 5 6 and optimization algorithms 7 ) require a shared memory architecture.
OpenQASM
OpenQASM 8 is the intermediate representation introduced by IBM for use with Qiskit and the IBM Q Experience.
Blackbird
Blackbird 9 10 is a quantum instruction set and intermediate representation used by Xanadu Quantum Technologies and Strawberry Fields. It is designed to represent continuous-variable quantum programs that can run on photonic quantum hardware.
Quantum Software Development Kits
Quantum software development kits provide collections of tools to create and manipulate quantum programs. 11 They also provide the means to simulate the quantum programs or prepare them to be run using cloud-based quantum devices and self-hosted quantum devices.
SDKs with access to quantum processors
The following software development kits can be used to run quantum circuits on prototype quantum devices, as well as on simulators.
Perceval
An open-source project created by Quandela fr for designing photonic quantum circuits and developing quantum algorithms, based on Python. Simulations are run either on the user's own computer or on the cloud. Perceval is also used to connect to Quandela's cloud-based photonic quantum processor. 12 13
Ocean
An Open Source suite of tools developed by D-Wave. Written mostly in the Python programming language, it enables users to formulate problems in Ising Model and Quadratic Unconstrained Binary Optimization formats (QUBO). Results can be obtained by submitting to an online quantum computer in Leap, D-Wave's real-time Quantum Application Environment, customer-owned machines, or classical samplers.
ProjectQ
An Open Source project developed at the Institute for Theoretical Physics at ETH, which uses the Python programming language to create and manipulate quantum circuits. 14 Results are obtained either using a simulator, or by sending jobs to IBM quantum devices.
Qiskit
An Open Source project developed by IBM. 15 Quantum circuits are created and manipulated using Python. Results are obtained either using simulators that run on the user's own device, simulators provided by IBM or prototype quantum devices provided by IBM. As well as the ability to create programs using basic quantum operations, higher level tools for algorithms and benchmarking are available within specialized packages. 16 Qiskit is based on the OpenQASM standard for representing quantum circuits. It also supports pulse level control of quantum systems via QiskitPulse standard. 17
Qibo
An open source full-stack API for quantum simulation, quantum hardware control and calibration developed by multiple research laboratories, including QRC, CQT and INFN. Qibo is a modular framework which includes multiple backends for quantum simulation and hardware control. 18 19 This project aims at providing a platform agnostic quantum hardware control framework with drivers for multiple instruments 20 and tools for quantum calibration, characterization and validation. 21 This framework focuses on self-hosted quantum devices by simplifying the software development required in labs.
Forest
An open source project developed by Rigetti, which uses the Python programming language to create and manipulate quantum circuits. Results are obtained either using simulators or prototype quantum devices provided by Rigetti. As well as the ability to create programs using basic quantum operations, higher level algorithms are available within the Grove package. 22 Forest is based on the Quil instruction set.
t|ket>
A quantum programming environment and optimizing compiler developed by Cambridge Quantum Computing that targets simulators and several quantum hardware back-ends, released in December 2018. 23
Strawberry Fields
An open-sourcePythonlibrary developed by Xanadu Quantum Technologies for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits. 24 25 Three simulators are provided - one in the Fock basis, one using the Gaussian formulation of quantum optics, 26 and one using the TensorFlow machine learning library. Strawberry Fields is also the library for executing programs on Xanadu's quantum photonic hardware. 27 28
PennyLane
An open-sourcePython library developed by Xanadu Quantum Technologies for differentiable programming of quantum computers. 29 30 31 32 PennyLane provides users the ability to create models using TensorFlow, NumPy, or PyTorch, and connect them with quantum computer backends available from IBMQ, Google Quantum, Rigetti, Quantinuum 33 and Alpine Quantum Technologies 34 . 35
Quantum Development Kit
A project developed by Microsoft 36 as part of the .NET Framework. Quantum programs can be written and run within Visual Studio and VSCode using the quantum programming language Q#. Programs developed in the QDK can be run on Microsoft's Azure Quantum, 37 and run on quantum computers from Quantinuum, 33 IonQ, and Pasqal. 38
Cirq
An Open Source project developed by Google, which uses the Python programming language to create and manipulate quantum circuits. Programs written in Cirq can be run on IonQ, Pasqal, 38 Rigetti, and Alpine Quantum Technologies. 34
Quantum Programming Languages
There are two main groups of quantum programming languages: imperative quantum programming languages and functional quantum programming languages.
Imperative Languages
The most prominent representatives of the imperative languages are QCL, 39 LanQ 40 and Q|SI>. 41
Ket
Ket 42 is an open-source embedded language designed to facilitate quantum programming, leveraging the familiar syntax and simplicity of Python. It serves as an integral component of the Ket Quantum Programming Platform, 43 seamlessly integrating with a Rust runtime library and a quantum simulator. Maintained by Quantuloop, the project emphasizes accessibility and versatility for researchers and developers. The following example demonstrates the implementation of a Bell state using Ket:
fromketimport*
a,b=quant(2)# Allocate two quantum bits
H(a)# Put qubit `a` in a superposition
cnot(a,b)# Entangle the two qubits in the Bell state
m_a=measure(a)# Measure qubit `a`, collapsing qubit `b` as well
m_b=measure(b)# Measure qubit `b`
# Assert that the measurement of both qubits will always be equal
assertm_a.value==m_b.value
QCL
Quantum Computation Language (QCL) is one of the first implemented quantum programming languages. 44 The most important feature of QCL is the support for user-defined operators and functions. Its syntax resembles the syntax of the C programming language and its classical data types are similar to primitive data types in C. One can combine classical code and quantum code in the same program.
Quantum pseudocode
Quantum pseudocode proposed by E. Knill is the first formalized language for description of quantum algorithms. It was introduced and, moreover, was tightly connected with a model of quantum machine called Quantum Random Access Machine (QRAM).
Q#
A language developed by Microsoft to be used with the Quantum Development Kit. 45
Q|SI>
Q|SI> is a platform embedded in .Net language supporting quantum programming in a quantum extension of while-language. 41 46 This platform includes a compiler of the quantum while-language 47 and a chain of tools for the simulation of quantum computation, optimisation of quantum circuits, termination analysis of quantum programs, 48 and verification of quantum programs. 49 50
Q language
Q Language is the second implemented imperative quantum programming language. 51 Q Language was implemented as an extension of C++ programming language. It provides classes for basic quantum operations like QHadamard, QFourier, QNot, and QSwap, which are derived from the base class Qop. New operators can be defined using C++ class mechanism.
Quantum memory is represented by class Qreg.
Qregx1;// 1-qubit quantum register with initial value 0
Qregx2(2,0);// 2-qubit quantum register with initial value 0
The computation process is executed using a provided simulator. Noisy environments can be simulated using parameters of the simulator.
qGCL
Quantum Guarded Command Language (qGCL) was defined by P. Zuliani in his PhD thesis. It is based on Guarded Command Language created by Edsger Dijkstra.
It can be described as a language of quantum programs specification.
QMASM
Quantum Macro Assembler (QMASM) is a low-level language specific to quantum annealers such as the D-Wave. 52
Scaffold
Scaffold is C-like language, that compiles to QASM and OpenQASM. It is built on top of the LLVM Compiler Infrastructure to perform optimizations on Scaffold code before generating a specified instruction set. 53 54
Silq
Silq is a high-level programming language for quantum computing with a strong static type system, developed at ETH Zürich. 55 56
LQP
The Logic of Quantum Programs (LQP) is a dynamic quantum logic, capable of expressing important features of quantum measurements and unitary evolutions of multi-partite states, and provides logical characterizations of various forms of entanglement. The logic has been used to specify and verify the correctness of various protocols in quantum computation. 57 58
Functional Languages
Efforts are underway to develop functional programming languages for quantum computing. Functional programming languages are well-suited for reasoning about programs. Examples include Selinger's QPL, 59 and the Haskell-like language QML by Altenkirch and Grattage. 60 61 Higher-order quantum programming languages, based on lambda calculus, have been proposed by van Tonder, 62 Selinger and Valiron 63 and by Arrighi and Dowek. 64
QFC and QPL
QFC and QPL are two closely related quantum programming languages defined by Peter Selinger. They differ only in their syntax: QFC uses a flow chart syntax, whereas QPL uses a textual syntax. These languages have classical control flow but can operate on quantum or classical data. Selinger gives a denotational semantics for these languages in a category of superoperators.
QML
QML is a Haskell-like quantum programming language by Altenkirch and Grattage. 65 60 Unlike Selinger's QPL, this language takes duplication, rather than discarding, of quantum information as a primitive operation. Duplication in this context is understood to be the operation that maps | ϕ ⟩to | ϕ ⟩⊗ | ϕ ⟩, and is not to be confused with the impossible operation of cloning; the authors claim it is akin to how sharing is modeled in classical languages. QML also introduces both classical and quantum control operators, whereas most other languages rely on classical control.
An operational semantics for QML is given in terms of quantum circuits, while a denotational semantics is presented in terms of superoperators, and these are shown to agree. Both the operational and denotational semantics have been implemented (classically) in Haskell. 66
LIQUi|>
LIQUi|> (pronounced liquid) is a quantum simulation extension on the F# programming language. 67 It is currently being developed by the Quantum Architectures and Computation Group (QuArC) 68 part of the StationQ efforts at Microsoft Research. LIQUi|> seeks to allow theorists to experiment with quantum algorithm design before physical quantum computers are available for use. 69
It includes a programming language, optimization and scheduling algorithms, and quantum simulators. LIQUi|> can be used to translate a quantum algorithm written in the form of a high-level program into the low-level machine instructions for a quantum device. 70
Quantum lambda calculi
Quantum lambda calculi are extensions of the classical lambda calculus introduced by Alonzo Church and Stephen Cole Kleene in the 1930s. The purpose of quantum lambda calculi is to extend quantum programming languages with a theory of higher-order functions.
The first attempt to define a quantum lambda calculus was made by Philip Maymin in 1996. 71 His lambda-q calculus is powerful enough to express any quantum computation. However, this language can efficiently solve NP-complete problems, and therefore appears to be strictly stronger than the standard quantum computational models (such as the quantum Turing machine or the quantum circuit model). Therefore, Maymin's lambda-q calculus is probably not implementable on a physical device citation needed .
In 2003, André van Tonder defined an extension of the lambda calculus suitable for proving correctness of quantum programs. He also provided an implementation in the Scheme programming language. 72
In 2004, Selinger and Valiron defined a strongly typed lambda calculus for quantum computation with a type system based on linear logic. 73
Quipper
Quipper was published in 2013. 74 75 It is implemented as an embedded language, using Haskell as the host language. 76 For this reason, quantum programs written in Quipper are written in Haskell using provided libraries. For example, the following code implements preparation of a superposition
importQuipper
spos::Bool->CircQubit
sposb=doq<-qinitb
r<-hadamardq
returnr
Wikipedia Quantum Programming
Disclaimer: Some of the contents of this webpage have been taken from various sources on the internet. If you find any content that should be removed from this site because of Copyrights, please send a message and it will be promptly removed.
|
|
|
|
Home/ Info/ Products/ BIG TECH Metaverse Metaverse Vs. Virtual Reality PC Buyers Guide/ IEEE 802 Standards Social Media Platforms Computer & IT Certifications Processor Generations Memory DDR3 Vs. DDR4 SSD Vs. HDD SAS vs. SATA HTML 5G Android Tips and Tricks STEM Business Intelligence Tools Web Intelligence Quantum Computing Datafication Artificial Intelligence (AI) A.I. Jobs Cognitive Technology ChatGPT WorldCoin Robotics Internet of Things (IOT) Web Of Things (WoT) Renewable Energy Nano Technology Cleantech Ag/Agro/Agri Tech 3D Printing Office Suites Windows Run Commands Hiren's Boot CD Benchmarks Android Vs. IOS Mac Vs. PC Mac Keyboard Shortcuts Linux CLi Commands Windows 11 Requirements Venus Project/ Computer Security and Law Techno Lingo Encyclopedias Search Engines Glossary Online Jobs Contact
Active Components Passive Components Test Electrical Components Electronics Classification Nuclear Energy Electric Vehicles (EVs)
Education Technology (EdTech) AWS Certification Google Certification Oracle Certifications cisco certifications Huawei Certification Microsoft Certifications Linux Certification Business Certifications YouTube Learning Channels
Google-Cloud-Platform-Guide Amazon-Web-Services-Guide Global-Cloud-Infrastructure-Of-AWS Amazon-Web-Services-Cli-Guide AWS-Cloudformation AWS Tools AWS Marketplace Devops Microsoft-Azure Oracle-Cloud Digitalocean-Cloud Openstack-Cloud IaC CloudFormation Anatomy Terraform Summary Edge Vs. Cloud Vs. Fog Computing Security Topics
Certified Enterprise Blockchain Professional (CEBP) Web 3.0 Satoshi Nakamoto Cryptocurrency Dark Web Ethereum NFT Merkle Tree El-Salvador eNaira Challenges Of Crypto To Cash
Web C++ JAVA Python Python Glossary Angular.js Scala
Copyright BICT Solutions Privacy Policy. | Terms and Conditions apply | All rights reserved.