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IBM Quantum Experience
https://research.ibm.com/interactive/system-one/

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Aquije Quiroga, Luis Enrique (u202114936)

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Aquije Quiroga, Luis Enrique (u202114936)

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Simulación cuántica usando Cirq y qsim en Google Cloud | Cloud Architecture Center

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En los siguientes pasos, crearás una instancia de VM optimizada para contenedores que usarás a fin de ejecutar las simulaciones cuánticas de qsim.

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El QDK proporciona un simulador de estado completo que simula una máquina cuántica en el equipo local. Puede usar el simulador de estado completo para ejecutar y depurar algoritmos cuánticos escritos en Q#, utilizando hasta 30 cúbits. La funcionalidad del simulador de estado completo es similar al simulador cuántico que se usa en la plataforma LIQ$Ui|\rangle$ de Microsoft Research.

Invocación y ejecución del simulador de estado completo

El simulador de estado completo se expone con la clase QuantumSimulator. Para más información, consulte Formas de ejecutar un programa de Q#.

Invocación del simulador desde C#

Cree una instancia de la clase QuantumSimulator y pásela al método Run de una operación cuántica, junto con los parámetros adicionales.

 using (var sim = new QuantumSimulator()) { var res = myOperation.Run(sim).Result; ///... }

Dado que la clase QuantumSimulator implementa la interfaz IDisposable, debe llamar al método Dispose una vez que ya no necesite la instancia del simulador. La mejor manera de hacerlo es encapsular la declaración y las operaciones del simulador dentro de una instrucción using , que llama automáticamente al Dispose método .

Invocación del simulador desde Python

Use el método simulate() de la biblioteca de Python de Q# con la operación de Q# importada:

qubit_result = myOperation.simulate()

Invocación del simulador desde la línea de comandos

Cuando se ejecuta un Q# programa desde la línea de comandos, el simulador de estado completo es el equipo predeterminado target . Opcionalmente, puede usar el parámetro --simulator (o -s shortcut) para especificar la máquina deseada target . Los dos comandos siguientes ejecutan un programa mediante el simulador de estado completo.

dotnet run dotnet run -s QuantumSimulator

Invocación del simulador desde Jupyter Notebooks

Use el comando magic %simulate de IQ# para ejecutar la operación de Q#.

%simulate myOperation

Inicialización del simulador

De forma predeterminada, el simulador de estado completo usa un generador de números aleatorios para simular la aleatoriedad cuántica. Con fines de prueba, a veces resulta útil tener resultados deterministas. En un programa de C#, puede hacerlo proporcionando un valor de inicialización al generador de números aleatorios en el constructor QuantumSimulator con el parámetro randomNumberGeneratorSeed.

 using (var sim = new QuantumSimulator(randomNumberGeneratorSeed: 42)) { var res = myOperationTest.Run(sim).Result; ///... }

Opciones del simulador

El comportamiento del simulador de estado completo se puede ajustar al constructor de C# a través de los parámetros siguientes:

throwOnReleasingQubitsNotInZeroState
El simulador puede advertirle si los cúbits no se han devuelto al estado antes de liberarse zero iniciando una excepción. El valor predeterminado es true.

randomNumberGeneratorSeed
Obtenga el comportamiento determinista mediante la propagación del simulador.

disableBorrowing
Si no desea usar cúbits prestados para esta simulación, puede deshabilitar esta característica estableciendo este parámetro trueen . En su lugar, los cúbits prestados se reemplazarán por cúbits limpios normales. El valor predeterminado es false.

Nota

Restablecer o medir cúbits antes de que la especificación requiera la Q# versión; no hacerlo puede provocar errores computacionales.

El código siguiente muestra una posible configuración de los parámetros.

 var sim = new QuantumSimulator ( throwOnReleasingQubitsNotInZeroState: false, randomNumberGeneratorSeed: 42, disableBorrowing: true )

Configuración de subprocesos

El simulador de estado completo usa OpenMP para paralelizar el álgebra lineal requerida. De forma predeterminada, OpenMP usa todos los subprocesos de hardware disponibles, lo que significa que los programas con un número reducido de cúbits a menudo se ejecutan lentamente porque la coordinación necesaria enana el trabajo real. Puede corregirlo estableciendo la variable de entorno OMP_NUM_THREADS en un número pequeño. Como regla general, configure un subproceso para hasta cuatro cúbits y, a continuación, un subproceso adicional por cúbit. Es posible que tenga que ajustar la variable en función del algoritmo.

Consulte también

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Simulador cuántico de estado completo - Azure Quantum

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Obtenga información sobre cómo ejecutar Q# los programas en el simulador de estado completo de Microsoft Quantum Development Kit .

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http://quantum-studio.net/
https://quest.qtechtheory.org/
https://learn.microsoft.com/es-es/azure/quantum/machines/toffoli-simulator

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https://quantumai.google/qsim

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Ramirez Mendez, Sebastian Andre - u20191e575

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Ramirez Mendez, Sebastian Andre - u20191e575

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https://cloud.google.com/architecture/quantum-simulation-on-google-cloud-with-cirq-qsim?hl=es-419

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Simulación cuántica usando Cirq y qsim en Google Cloud | Cloud Architecture Center

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En los siguientes pasos, crearás una instancia de VM optimizada para contenedores que usarás a fin de ejecutar las simulaciones cuánticas de qsim.

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Disertación Escrita_protected.pdf (puce.edu.ec)

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https://qiskit.org/documentation/tutorials/simulators/1_aer_provider.html

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Introduction

This notebook shows how to import the Qiskit Aer simulator backend and use it to run ideal (noise free) Qiskit Terra circuits.

import numpy as np

# Import Qiskit
from qiskit import QuantumCircuit
from qiskit import Aer , transpile
from qiskit.tools.visualization import plot_histogram , plot_state_city
import qiskit.quantum_info as qi

The Aer Provider

The Aer provider contains a variety of high performance simulator backends for a variety of simulation methods. The available backends on the current system can be viewed using Aer.backends

[AerSimulator('aer_simulator'),
 AerSimulator('aer_simulator_statevector'),
 AerSimulator('aer_simulator_density_matrix'),
 AerSimulator('aer_simulator_stabilizer'),
 AerSimulator('aer_simulator_matrix_product_state'),
 AerSimulator('aer_simulator_extended_stabilizer'),
 AerSimulator('aer_simulator_unitary'),
 AerSimulator('aer_simulator_superop'),
 QasmSimulator('qasm_simulator'),
 StatevectorSimulator('statevector_simulator'),
 UnitarySimulator('unitary_simulator'),
 PulseSimulator('pulse_simulator')]

The Aer Simulator

The main simulator backend of the Aer provider is the AerSimulator backend. A new simulator backend can be created using Aer.get_backend('aer_simulator') .

simulator = Aer . get_backend ( 'aer_simulator' )

The default behavior of the AerSimulator backend is to mimic the execution of an actual device. If a QuantumCircuit containing measurements is run it will return a count dictionary containing the final values of any classical registers in the circuit. The circuit may contain gates, measurements, resets, conditionals, and other custom simulator instructions that will be discussed in another notebook.

Simulating a quantum circuit

The basic operation runs a quantum circuit and returns a counts dictionary of measurement outcomes. Here we run a simple circuit that prepares a 2-qubit Bell-state \(\left|\psi\right\rangle = \frac{1}{\sqrt{2}}\left(\left|0,0\right\rangle + \left|1,1 \right\rangle\right)\) and measures both qubits.

Returning measurement outcomes for each shot

The QasmSimulator also supports returning a list of measurement outcomes for each individual shot. This is enabled by setting the keyword argument memory=True in the run .

Aer Simulator Options

The AerSimulator backend supports a variety of configurable options which can be updated using the set_options method. See the AerSimulator API documentation for additional details.

Simulation Method

The AerSimulator supports a variety of simulation methods, each of which supports a different set of instructions. The method can be set manually using simulator.set_option(method=value) option, or a simulator backend with a preconfigured method can be obtained directly from the Aer provider using Aer.get_backend .

When simulating ideal circuits, changing the method between the exact simulation methods stabilizer , statevector , density_matrix and matrix_product_state should not change the simulation result (other than usual variations from sampling probabilities for measurement outcomes)

Automatic Simulation Method

The default simulation method is automatic which will automatically select a one of the other simulation methods for each circuit based on the instructions in those circuits. A fixed simulation method can be specified by by adding the method name when getting the backend, or by setting the method option on the backend.

GPU Simulation

The statevector , density_matrix and unitary simulators support running on a NVidia GPUs. For these methods the simulation device can also be manually set to CPU or GPU using simulator.set_options(device='GPU') backend option. If a GPU device is not available setting this option will raise an exception.

The Aer provider will also contain preconfigured GPU simulator backends if Qiskit Aer was installed with GPU support on a compatible system:

aer_simulator_statevector_gpu

aer_simulator_density_matrix_gpu

aer_simulator_unitary_gpu

Note: The GPU version of Aer can be installed using pip install qiskit-aer-gpu .

Simulation Precision

One of the available simulator options allows setting the float precision for the statevector , density_matrix , unitary and superop methods. This is done using the set_precision="single" or precision="double" (default) option:

Setting the simulation precision applies to both CPU and GPU simulation devices. Single precision will halve the required memory and may provide performance improvements on certain systems.

Custom Simulator Instructions

Saving the simulator state

The state of the simulator can be saved in a variety of formats using custom simulator instructions.

Circuit method

Description

Supported Methods

Save the simulator state in the native format for the simulation method

All

Note that these instructions are only supported by the Aer simulator and will result in an error if a circuit containing them is run on a non-simulator backend such as an IBM Quantum device.

Saving the final statevector

To save the final statevector of the simulation we can append the circuit with the save_statevector instruction. Note that this instruction should be applied before any measurements if we do not want to save the collapsed post-measurement state

Saving the circuit unitary

To save the unitary matrix for a QuantumCircuit we can append the circuit with the save_unitary instruction. Note that this circuit cannot contain any measurements or resets since these instructions are not supported on for the "unitary" simulation method

Saving multiple states

We can also apply save instructions at multiple locations in a circuit. Note that when doing this we must provide a unique label for each instruction to retrieve them from the results

Setting the simulator to a custom state

The AerSimulator allows setting a custom simulator state for several of its simulation methods using custom simulator instructions

Circuit method

Description

Supported Methods

Notes: * These instructions must be applied to all qubits in a circuit, otherwise an exception will be raised. * The input state must also be a valid state (statevector, density matrix, unitary etc) otherwise an exception will be raised. * These instructions can be applied at any location in a circuit and will override the current state with the specified one. Any classical register values (e.g. from preceding measurements) will be unaffected * Set state instructions are only supported by the Aer simulator and will result in an error if a circuit containing them is run on a non-simulator backend such as an IBM Quantum device.

Setting a custom statevector

The set_statevector instruction can be used to set a custom Statevector state. The input statevector must be valid (\(|\langle\psi|\psi\rangle|=1\))

Using the initialize instruction

It is also possible to initialize the simulator to a custom statevector using the initialize instruction. Unlike the set_statevector instruction this instruction is also supported on real device backends by unrolling to reset and standard gate instructions.

Setting a custom density matrix

The set_density_matrix instruction can be used to set a custom DensityMatrix state. The input density matrix must be valid (\(Tr[\rho]=1, \rho \ge 0\))

Setting a custom stabilizer state

The set_stabilizer instruction can be used to set a custom Clifford stabilizer state. The input stabilizer must be a valid Clifford .

Setting a custom unitary

The set_unitary instruction can be used to set a custom unitary Operator state. The input unitary matrix must be valid (\(U^\dagger U=\mathbb{1}\))

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Simulators - Qiskit 0.43.1 documentation

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This notebook shows how to import the Qiskit Aer simulator backend and use it to run ideal (noise free) Qiskit Terra circuits. import numpy as np # Import Qiskit from qiskit import QuantumCircuit from qiskit import Aer , transpile from qiskit.tools.visualization import plot_histogram , plot_state_city import qiskit.quantum_info as qi The Aer provider contains a variety of high performance simulator backends for a variety of simulation methods.

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Un «¡Hola, mundo!» en siete lenguajes de programación cuántica

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from qiskit import QuantumCircuit # Create a basic Bell State. qc = QuantumCircuit(2, 2) # 2 qubits, 2 bits qc.h(0) qc.cx(0, 1) # Measure and put the result into the two bits. qc.measure([0, 1], [0, 1]) # Display circuit. print(qc) En el…

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Simulación cuántica usando Cirq y qsim en Google Cloud | Cloud Architecture Center

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En los siguientes pasos, crearás una instancia de VM optimizada para contenedores que usarás a fin de ejecutar las simulaciones cuánticas de qsim.

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https://quantumai.google/cirq

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import cirq

# Pick a qubit.
qubit = cirq.GridQubit(0, 0)

# Create a circuit
circuit = cirq.Circuit(
    cirq.X(qubit)**0.5,  # Square root of NOT.
    cirq.measure(qubit, key='m')  # Measurement.
)
print("Circuit:")
print(circuit)

# Simulate the circuit several times.
simulator = cirq.Simulator()
result = simulator.run(circuit, repetitions=20)
print("Results:")
print(result)

Get started on Cirq's basics with this tutorial. Learn the difference between gates and operations and how to build circuits out of moments. Simulate the resulting quantum circuits and find out how to perform transformations on these circuits.

Learn how to write a quantum approximate optimization algorithm for NISQ hardware by writing a variational algorithm to optimize a solution to max-cut, which is a hard problem for classical computing.

We host virtual open source meetings every week. Join our email lists to get invited to the ongoing open source meetings relevant to your work.

Whether you are a researcher who wants to push the boundaries of what's available for NISQ computers, a software engineer, a technical writer, or a student who is excited about quantum computing, we welcome your contributions to our open source code available on GitHub.

If you are looking for something to help out in Cirq, see the list of good first issues to get started.

For larger features, check out our RFC process to learn how to contribute.

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Cirq is a Python software library for writing, manipulating, and optimizing quantum circuits, and then running them on quantum computers and quantum simulators.

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https://www.ibm.com/quantum

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IBM Quantum leads the world in quantum computing. This technology is widely expected to solve valuable problems that are unsolvable using any known methods on classical supercomputers.

With the largest quantum computing fleet in the world and Qiskit Runtime - our quantum computing service and programming model - it's never been easier to execute advanced quantum programs with IBM.

We built Qiskit Runtime for the era of useful quantum computing, allowing you to easily create powerful algorithms and run them at scale. Qiskit Runtime delivers a tremendous performance increase for non-trivial workloads, resulting in some cases in a 120x speedup over previous methods. First-of-their-kind quantum primitives and middleware enhance its capabilities as we work toward realizing quantum advantage.

Our community of clients and partners comprises 210+ Fortune 500 companies, academic institutions, national labs, and startups - all striving toward this same goal.

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IBM Quantum is building quantum computers to solve today's unsolvable problems. See our updated roadmap to learn more about where we're headed through 2025.

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https://www.youtube.com/watch?v=YE4m3yCdcqE

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Learn to build your first quantum solution with the Quantum Development Kit and Q#  : Build 2018

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https://grow-self.com/quantum-computing-programming-languages/

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Quantum computing is in its infancy, but, though still far from mainstream, it's quickly becoming more relevant than ever. Some experts believe that quantum computing will soon become the primary way we use computers as we know them today.

Disclaimer: This article is written for beginners who want to get into learning Quantum Computing, not for experienced professionals. If there are any mistakes in the content or you think it can be improved, please let us know in the comments below.

What exactly is a Quantum Computer?
It's a device made up of tiny particles (electrons) held together in a specific way by magnetic fields or lasers. These particles can be thought of as behaving like atoms with their own internal states and properties-but they're also capable of storing information about themselves even when they're not interacting with other objects or people!
Quantum computers are more powerful than classical computers because they can perform calculations that classical ones cannot. They do this by manipulating Qubits (quantum bits), which can be either 0 or 1 at any given moment and are essentially tiny versions of binary digits used in traditional computing systems.

A Qubit has two states: a Zero(0) state represented by a spin-up orientation and a One(1) state represented by a spin-down orientation. The two orientations represent different possible outcomes for any given calculation. This means that if you have one Qubit (quantum bit), then there are countless combinations possible for its state at any given time; hence why it can perform certain tasks faster than traditional computers powered by binary digits could ever dream about doing before.

Programming languages for quantum computing

Quantum computers are not widely available yet, but you can start learning to code in one now. Learning quantum computing programming languages can be challenging. The number of programming languages used regularly increases as the field of quantum computing grows.

🎯Also read: How to self-learn Quantum Computing?

While many different languages are being used, some are more widely used than others. Python is the most popular language for quantum computing and has been used in scientific projects since 2011. Qasm is a scripting language that uses Python syntax and features to create programs that can be run on any computer with no modifications required. C++ was also developed for use in quantum computing but lost popularity after it was found to have problems with error handling and lack of modularity compared to other languages such as Python or Haskell.

Haskell is another popular choice among developers due to its simplicity when compared with other programming paradigms like Java or C++ which makes it easy for new programmers who have little experience writing code before jumping into this world full force. without having any prior knowledge about what they're doing beforehand!

So, if you're interested in learning about quantum computing and its impact on the future of computing, programming languages are an excellent place to start. However, don't let the number of different languages intimidate you! There are only a handful of languages with recognized quantum computing implementations - all of which have their own strengths and weaknesses.
Let's take a closer look at some popular ones:
Python is a general-purpose programming language that can be used for many different tasks. It's also one of the most popular options for learning quantum computing because it's easy to learn and versatile enough to work with the quantum world.

Python has a large community of developers who provide support through online forums and meetups across the globe. This makes it easier to find help when you need it most-and if you're looking for more than just answers from someone else, there are plenty of tutorials on how best to use Python in your projects!

Qasm is a quantum assembly language and a high-level programming language for quantum computers. It can be used to program quantum computers and is built on top of the C++ programming language. The main goal of Qasm is to provide an easy-to-use language that allows you to write code in a declarative manner, which makes it much easier than writing classical assembly languages or machine code.

Qasm was created by scientists at Google Brain and Microsoft Research Redmond as part of their effort to develop new ways of programming their quantum computers (which we'll talk about later). As such, it has been made available under an open-source license so that anyone who wants access can do so-but don't worry if you're not interested in contributing back; it's still free!

C++ is a general-purpose programming language that was developed by Bjarne Stroustrup. It is used for many different types of applications, including games and video games. The main advantage of this language over other languages like Python or Java is its high level of abstraction, making it easier to write code in C++ than in any other language out there.

The syntax of C++ can be confusing at first because it uses some unique terms such as lambdas (functions) and objects (data structures). However, once you get used to them, they become second nature!

QCL is a functional programming language that's designed to be used on quantum computers. It's built to work with Qubits, which are quantum bits (qubits) of information that can store 0 and 1 at the same time. This allows them to perform sophisticated operations like addition, multiplication, and bitwise operators without losing any information about their state-a feature called "entanglement."

QCL was created by scientists at the University of Waterloo in Canada who wanted an easier way for programmers to write programs for their devices. It's been used since 2016 by researchers working on quantum computing systems; today, more than 100 people are working on it full-time!

5. Haskell and OCaml

Haskell and OCaml are both functional languages, which means that they're built around functions. Haskell has a much richer type system than most other programming languages-it lets you define a function as anything from an integer to a list of lists!

These two languages are used for quantum computing in the same way that Pascal and C were used for early computers. You can use them to build algorithms or simulate molecules; however, they aren't designed specifically for this purpose (although some people have done some amazing work using these tools). You can read about the differences between Haskell and OCaml here - Haskell v OCaml.

Q# (Q-Sharp) is a programming language designed specifically around quantum computing. It allows developers who still need to gain expertise in this area to quickly get started with coding algorithms that can be executed on real quantum hardware. Q# was released by Microsoft as an open-source, high-level programming language that can be used to write quantum algorithms at scale prioritizing execution time. Microsoft released an open-source project called Quantum Katas, which you can use to self-learn Q# at your own pace.

Qiskit was IBM's gift to the quantum programming world in 2017. An open-source Software Development Kit for working with quantum computers at the level of circuits, pulses, and algorithms, Qiskit was developed by IBM Research and the wider Qiskit community and provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Experience or on simulators on a local computer.

The wide use of quantum computers is just around the corner. If you want to be a part of the quantum computing bright future, take your pick from this list of languages to learn and dip your toes into the quantum computing universe.

Sources: https://thequantuminsider.com/2022/07/28/state-of-quantum-computing-programming-languages-in-2022/https://en.wikipedia.org/wiki/Quantum_programminghttps://research.aimultiple.com/quantum-computing-programming/https://www.amarchenkova.com/posts/programming-for-quantum-computinghttps://www.nature.com/articles/d41586-021-00533-xhttps://quantumcomputing.stackexchange.com/questions/1474/what-programming-languages-are-available-for-quantum-computers
Related articles: How To Self Learn Quantum Computing In 2023
9 Universities Offering Degree Programs In Quantum Computing
List Of The Best Quantum Computing Courses Online

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7 Quantum Computing Programming Languages to learn Quantum Computing - Grow-Self 🚀

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If you're interested in learning about quantum computing and its impact on the future of computing, quantum computing programming languages are an excellent place to start.

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https://quest.qtechtheory.org/

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Aquije Quiroga, Luis Enrique (u202114936)

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Aquije Quiroga, Luis Enrique (u202114936)

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https://qiskit.org/documentation/locale/es_UN/qc_intro.html

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La computación cuántica representa un nuevo paradigma en computación que utiliza los principios fundamentales de la mecánica cuántica para realizar cálculos. Si estás leyendo esto, sin duda habrás escuchado que la promesa de la computación cuántica radica en la posibilidad de realizar de manera eficiente un puñado de tareas como la factorización en números primos, simulación cuántica, búsqueda, optimización y programas algebraicos como el machine learning; cálculos que en tamaño están más allá de las capacidades incluso de las computadoras clásicas más grandes.

El poder de la computación cuántica se basa en dos pilares de la mecánica cuántica, la interferencia y el entrelazamiento que resaltan los aspectos ondulatorios y de partículas de la computación cuántica, respectivamente. Qiskit es un kit de desarrollo de software (Software Development Kit, SDK) para realizar cálculos cuánticos que utilizan estos principios de la mecánica cuántica usando el lenguaje de los circuitos cuánticos . Compuestos por compuertas cuánticas, instrucciones y lógica de control clásica, los circuitos cuánticos permiten expresar algoritmos complejos y aplicaciones de manera abstracta que pueden ser ejecutados en una computadora cuántica. En esencia, Qiskit es un motor de construcción, optimización y ejecución de circuitos cuánticos. Las capas de aplicaciones y algoritmos adicionales aprovechan los circuitos cuánticos, a menudo en combinación con recursos computacionales clásicos, para resolver problemas de optimización, química cuántica, física, machine learning y finanzas. A continuación, brindamos una breve descripción general de la computación cuántica y cómo se emplea Qiskit en cada paso. Los lectores interesados son dirigidos a materiales detallados adicionales para obtener más información.

Interferencia

Al igual que una computadora clásica, una computadora cuántica opera en bits. Sin embargo, mientras que los bits clásicos solo se pueden encontrar en los estados 0 y 1, un bit cuántico, o qubit, puede representar los valores 0 y 1, o combinaciones lineales de ambos. Estas combinaciones lineales se conocen como superposiciones (o estados de superposición).

Para ver cómo se utiliza este recurso en la computación cuántica, primero recurrimos a un análogo clásico: cancelación de ruido. La cancelación de ruido, como se hace en los auriculares con cancelación de ruido, por ejemplo, se realiza empleando la superposición y el principio de interferencia para reducir la amplitud del ruido no deseado generando un tono de aproximadamente la misma frecuencia y amplitud, pero desfasado por un valor de \(\pi\) (o cualquier otro múltiplo entero impar de \(\pi\)).

Como se muestra arriba, cuando la diferencia de fase está cerca de un múltiplo impar de \(\pi\), la superposición de las dos ondas da como resultado una interferencia y una salida que se reduce significativamente en comparación con la original. El resultado es la señal de interés libre de ruido. Aunque este procesamiento se realiza mediante circuitos digitales, la amplitud y la fase son variables continuas que nunca pueden coincidir perfectamente, lo que resulta en una corrección incompleta.

Un cálculo general en una computadora cuántica procede de manera muy similar a la cancelación de ruido. Para empezar, se prepara una superposición de todos los posibles estados de cálculo. Esto luego se usa como entrada a un circuito cuántico que interfiere selectivamente los componentes de la superposición de acuerdo con un algoritmo prescrito. Lo que queda después de cancelar las amplitudes y fases relativas del estado de entrada es la solución al cálculo realizado por el circuito cuántico.

Entrelazamiento

El segundo principio de la mecánica cuántica que puede utilizar la computación cuántica es el fenómeno del entrelazamiento. El entrelazamiento se refiere a estados de más de un qubit (o partículas en general) en los que el estado combinado de los qubits contiene más información que la de los qubits individualmente. La inmensa mayoría de los estados cuánticos de varios qubits están entrelazados y representan un recurso valioso. Por ejemplo, los estados entrelazados entre qubits se pueden usar para la teleportación cuántica, donde un estado entrelazado compartido de dos qubits se puede manipular para transferir información de un qubit a otro, independientemente de la proximidad física relativa de los qubits. Los estados entrelazados, como estados naturales de los sistemas cuánticos, también son importantes en disciplinas como la química cuántica y la simulación cuántica, donde la(s) solución(es) a menudo toman la forma de estados de múltiples qubits entrelazados. También se pueden utilizar estados cuánticos altamente entrelazados de múltiples qubits para, por ejemplo, generar números aleatorios certificables. ¡Incluso hay un paquete de Qiskit para hacer esto!

Circuitos cuánticos

Los algoritmos y las aplicaciones que utilizan recursos de la mecánica cuántica se pueden escribir de manera fácil y eficiente en el lenguaje de circuitos cuánticos. Un circuito cuántico es una rutina computacional que consiste en operaciones cuánticas coherentes sobre datos cuánticos, como el que se mantiene en qubits, y computación clásica concurrente en tiempo real. Cada línea horizontal o cable en un circuito representa un qubit, siendo el extremo izquierdo del cable los datos cuánticos iniciales y el derecho los datos cuánticos finales generados por el cálculo del circuito cuántico. Las operaciones en qubits se pueden colocar en estos cables y se representan mediante cajas.

Los circuitos cuánticos permiten a una computadora cuántica tomar información clásica y generar una solución clásica, aprovechando principios cuánticos como la interferencia y el entrelazamiento para realizar el cálculo.

Un flujo de trabajo típico de un algoritmo cuántico consiste en:

El problema que queremos resolver,

Un algoritmo clásico que genera una descripción de un circuito cuántico,

El circuito cuántico que necesita ser ejecutado en un hardware cuántico,

Y la solución clásica que resulta del problema que este produce.

Las compuertas cuánticas forman las operaciones primitivas sobre datos cuánticos. Las compuertas cuánticas representan transformaciones reversibles que preservan la información de los datos cuánticos almacenados en qubits. Estas transformaciones "unitarias" representan el núcleo mecánico cuántico de un circuito cuántico. Algunas compuertas como \(X\) (también escrito como \(\oplus\)) y \(CX\) tienen análogos clásicos como las operaciones de cambio de bit (bit-flip) y \(XOR\), respectivamente, mientras que otras no. La compuerta Hadamard (\(H\)), junto con las rotaciones parametrizadas \(rX(\theta)\) y \(rY(\theta)\), generan estados de superposición, mientras que las compuertas como \(Z\), \(rZ(\theta)\), \(S\), y \(T\) imparten fases que pueden usarse para la interferencia. Las compuertas de dos qubit como la compuerta \(CX\) se utilizan para generar entrelazamientos entre pares de qubits, o para "patear" la fase de un qubit a otro. A diferencia de las compuertas, las operaciones como la "medición", representadas por el símbolo de medidor en una caja con una línea que se conecta a un cable "objetivo", extraen información parcial sobre el estado de un qubit, a menudo perdiendo la fase, para poder representarlo como un bit clásico y escribir ese bit clásico en el cable de destino (a menudo un cable completamente clásico en algún dispositivo de lectura). Esta es la forma típica de llevar información de los datos cuánticos a un dispositivo clásico. Ten en cuenta que con solo \(H\), \(rZ(\theta)\), \(CX\), y compuertas de medición, es decir, un conjunto de compuertas universales, podemos construir cualquier circuito cuántico, incluidos los que calculan eficazmente la dinámica de cualquier sistema físico de la naturaleza.

Algunas cargas de trabajo contienen una secuencia extendida de circuitos cuánticos intercalada con cálculos clásicos, por ejemplo, los algoritmos cuánticos variacionales ejecutan circuitos cuánticos dentro de un bucle de optimización que contiene procesamiento clásico. Para estas cargas de trabajo, el rendimiento del sistema aumenta significativamente si los circuitos cuánticos son parametrizados y las transiciones entre ejecución del circuito cuántico y cálculos clásicos se hacen eficientemente. Por esto, definimos el término computación en tiempo cercano (near-time computation) para referirnos a los cálculos con algoritmos que hacen uso de circuitos cuánticos de manera repetida con hardware desarrollado para acelerar los tiempos de computación. En la computación de tiempo cercano, los cálculos clásicos ocurren en una escala de tiempo más larga que los tiempos de coherencia de la computación cuántica. A diferencia de la computación en tiempo real (real-time computation), donde los cálculos clásicos ocurren dentro de los tiempos de decoherencia de un dispositivo cuántico.

La construcción de circuitos cuánticos complejos con el mínimo esfuerzo está el corazón de Qiskit, que admite un amplio conjunto de funciones de operaciones y puede enviar tus circuitos a una variedad de computadoras cuánticas o simuladores clásicos. Con solo unas pocas líneas de código, es posible construir circuitos complejos como el de arriba.

qr = QuantumRegister ( 3 , 'q' )
cr = ClassicalRegister ( 2 , 'zx_meas' )
qc = QuantumCircuit ( qr , cr )
qc . reset ( range ( 3 ))
qc . barrier ()
qc . h ( 1 )
qc . cx ([ 1 , 0 ],[ 2 , 1 ])
qc . h ( 0 )
qc . barrier ()
qc . measure ([ 0 , 1 ], [ 0 , 1 ])
qc . barrier ()
qc . z ( 2 ) . c_if ( cr , 1 )
qc . x ( 2 ) . c_if ( cr , 2 )

Computadoras cuánticas

Las computadoras cuánticas que se programan utilizando circuitos cuánticos pueden construirse a partir de cualquier tecnología cuántica que permita definir elementos de qubits y pueda implementar operaciones de compuertas de uno y varios qubits con alta fidelidad. En la actualidad, se están desarrollando activamente arquitecturas basadas en circuitos superconductores, iones atrapados, puntos cuánticos semiconductores, fotones y átomos neutros, y muchos son accesibles para los usuarios a través de Internet. Qiskit es agnóstico con respecto a la arquitectura subyacente de un sistema cuántico dado y puede compilar un circuito cuántico para que coincida con la topología de la compuerta de entrelazamiento de un dispositivo cuántico, mapear las instrucciones del circuito en el conjunto de compuertas nativas del dispositivo y optimizar el circuito cuántico resultante para mejorar la fidelidad.

Al igual que con el ejemplo anterior de cancelación de ruido, la amplitud y la fase de los qubits son grados de libertad continuos en los que las operaciones nunca se pueden realizar con exactitud. Estos errores de compuerta, junto con el ruido del entorno en el que reside una computadora cuántica, pueden conspirar para arruinar un cálculo si no se tienen en cuenta en el proceso de compilación, y pueden requerir procedimientos de mitigación adicionales para obtener una salida de alta fidelidad en los sistemas cuánticos actuales susceptibles al ruido. Qiskit es capaz de tener en cuenta una amplia gama de métricas de calibración de dispositivos (consulta la figura siguiente) en su estrategia de compilación y puede seleccionar un conjunto óptimo de qubits en el cual ejecutar un circuito cuántico determinado. Además, Qiskit alberga una colección de técnicas de mitigación de ruido para extraer una representación fiel de la salida de un circuito cuántico.

A dónde ir a partir de aquí

Esperamos haberle dado al lector una muestra de lo que la computación cuántica tiene para ofrecer y que estés ansioso por más. Si es así, existen varios recursos que pueden ser de interés:

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Computación cuántica en pocas palabras - documentación de Qiskit - 0.43.1

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La computación cuántica representa un nuevo paradigma en computación que utiliza los principios fundamentales de la mecánica cuántica para realizar cálculos.

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Curso para aprender programación cuántica

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Curso para aprender programación cuántica

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https://www.udemy.com/course/programacion-cuantica-para-programadores-no-cuanticos/?utm_source=adwords&utm_medium=udemyads&utm_campaign=DSA-WebIndex_la.ES_cc.LATAM&utm_term=_._ag_120316893258_._ad_504879908808_._kw__._de_c_._dm__._pl__._ti_dsa-1125582656112_._li_9060924_._pd__._&matchtype=&gclid=EAIaIQobChMIhbD64Pvb_wIVbIZaBR1EDgLuEAAYASAAEgIwG_D_BwE

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https://learn.microsoft.com/en-us/azure/quantum/overview-what-is-qsharp-and-qdk

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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 or formulate problems that run on quantum-inspired optimization solvers 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.

For other development environment options, see Setup the Quantum Development Kit.

The quantum programming language Q#

Q# is an open-source, high-level, programming language for developing and running quantum algorithms. It's 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 on GitHub.

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. For more information, see testing and debugging.

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.

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.

The following diagram shows the basic workflow after you submit your job:

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 and quantum-inspired optimization programs, and submit them to Azure Quantum to run on quantum hardware and optimization solvers.

Next Steps

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.

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Introduction to Q# & Quantum Development Kit - Azure Quantum

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Learn about the Q# programming language, the Quantum Development Kit (QDK), `and how you can create quantum programs.

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Language-Integrated Quantum Operations: LIQUi|>

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" width="300" height="186" srcset="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Liquid_Spotlight_292x180-300x186.jpg 300w, https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Liquid_Spotlight_292x180.jpg 540w" sizes="(max-width: 300px) 100vw, 300px" />LIQUi|> is a software architecture and toolsuite for quantum computing. 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. LIQUi|> is being developed by the Quantum Architectures and Computation Group (QuArC) at Microsoft Research.

About LIQUi|>

To aid in the development and understanding of quantum protocols, quantum algorithms, quantum error correction, and quantum devices, QuArC has developed an extensive software platform called LIQUi|>. LIQUi|> allows the simulation of Hamiltonians, quantum circuits, quantum stabilizer circuits, and quantum noise models, and supports Client, Service, and Cloud operation. It allows the user to express circuits in a high-level functional language (F#), and supports the extraction of circuit data structures that can be passed to other components for circuit optimization, quantum error correction, gate replacement, export or rendering. The system is architected to be fully modular to permit easy extension as desired.

LIQUi|> includes state-of-the-art circuit simulation of up to 30 qubits on a single machine with 32 GB RAM, limited only by memory and computing threads. The largest number factored to date on the simulator is a 13-bit number, which required 27 qubits, half a million gates, and 5 days runtime. The circuit was based on Beauregard's circuit for Shor's algorithm.

LIQUi|> can be used to translate a quantum algorithm written in the form of a high-level program into low-level machine instructions for a quantum device. The toolkit includes a compiler, optimizers, translators, various simulators and a host of examples. The complete platform is freely available on GitHub >

What LIQUi|> can do

You can use LIQUi|> to define quantum circuits, render them into a variety of graphical formats, and execute them using an appropriate simulator. Some of the specific algorithms you can simulate with LIQUi|> are:

Simple quantum teleportation

Shor's factoring algorithm

Quantum chemistry: computing the ground state energy of a molecule

Quantum error correction

Quantum associative memory (Ventura and Martinez)

Quantum linear algebra (Harrow, Hassidim, and Lloyd)

All of these algorithms, and many more, are included as samples with LIQUi|>.

Microsoft Quantum Challenge

Students worldwide devised real-world problems and came up with running solutions using the software simulator for LIQUi|>, in the first Quantum Computing Challenge >

Stay informed

To be notified of updates and other announcements, sign up on our listserv at:

To: listserv@lists.research.microsoft.com
Subject: Subscribe
SUB liquid-news FirstName LastName

Or, to subscribe anonymously:
SUB liquid-news anonymous

Contact us
liquid@microsoft.com

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Language-Integrated Quantum Operations: LIQUi|> - Microsoft Research

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LIQUi|> is a software architecture and toolsuite for quantum computing. 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.

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Silq: A High-level Quantum Programming Language (ethz.ch)

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Los desarrolladores de ETH Zurich, Suiza, introdujeron el primer lenguaje de programación de alto nivel para computadoras cuánticas, conocido como Silq .

El lenguaje aborda varios desafíos de los lenguajes cuánticos , como el código poco intuitivo y desordenado al admitir la descomputación segura y automática.

Para obtener una ventaja sobre los algoritmos tradicionales, los investigadores han estado trabajando en computadoras y algoritmos cuánticos durante algunas décadas.

Detrás del lenguaje

Silq es un lenguaje de programación de alto nivel para computación cuántica que está diseñado para extraer detalles de implementación de bajo nivel de algoritmos cuánticos.

La novedad técnica de este lenguaje es un sistema de tipo cuántico que captura aspectos importantes de los cálculos cuánticos y permite un cálculo seguro y automático, que es un desafío fundamental en los lenguajes cuánticos existentes.

Según los desarrolladores, Silq es el primer lenguaje cuántico con un fuerte sistema de tipos estáticos para proporcionar una semántica intuitiva, es decir, si un programa verifica el tipo, su semántica sigue una receta intuitiva que simplemente elimina valores temporales.

Este lenguaje permite expresar algoritmos cuánticos de forma más segura y concisa que los lenguajes de programación cuántica existentes.

Beneficios de Silq

La principal ventaja de Silq sobre los lenguajes cuánticos existentes es su descomputación segura y automática, habilitada por sus novedosas anotaciones const y qfree.

Los algoritmos Silq son más cortos y sencillos por naturaleza.

El lenguaje permite una semántica intuitiva pero física y previene estáticamente errores que no se detectan en los lenguajes cuánticos existentes.

Modifica el estado cuántico del programa de acuerdo con una semántica intuitiva que sigue las leyes de la física cuántica.

Silq evita la sobrecarga de notación asociada con los lenguajes que logran una menor seguridad estática en los programas.

Instalación

La forma recomendada de instalar Silq es mediante el complemento de código de Visual Studio (complemento vscode). Este enfoque funciona para Linux, Mac y Windows.

Para instalar el complemento vscode de Silq

Según los desarrolladores, a diferencia de los lenguajes cuánticos existentes como Q #, Quipper, ProjectQ, QWire, entre otros, este lenguaje soporta una visión descriptiva de los algoritmos cuánticos que expresa la intención de alto nivel del programador.

Las características clave del lenguaje también se pueden incorporar en idiomas existentes como QWire o Q #.

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Silq: El primer lenguaje de programación de alto nivel para computadoras cuánticas

# data[].attributes.attachment_link.description
Los desarrolladores de ETH Zurich, Suiza, introdujeron el primer lenguaje de programación de alto nivel para computadoras cuánticas, conocido como Silq . El lenguaje aborda varios desafíos de los lenguajes cuánticos , como el código poco intuitivo y desordenado al admitir la descomputación segura y automática.

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https://qiskit.org/documentation/locale/es_UN/intro_tutorial1.html

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https://learn.microsoft.com/es-es/azure/quantum/machines/full-state-simulator

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El QDK proporciona un simulador de estado completo que simula una máquina cuántica en el equipo local. Puede usar el simulador de estado completo para ejecutar y depurar algoritmos cuánticos escritos en Q#, utilizando hasta 30 cúbits. La funcionalidad del simulador de estado completo es similar al simulador cuántico que se usa en la plataforma LIQ$Ui|\rangle$ de Microsoft Research.

Invocación y ejecución del simulador de estado completo

El simulador de estado completo se expone con la clase QuantumSimulator. Para más información, consulte Formas de ejecutar un programa de Q#.

Invocación del simulador desde C#

Cree una instancia de la clase QuantumSimulator y pásela al método Run de una operación cuántica, junto con los parámetros adicionales.

 using (var sim = new QuantumSimulator()) { var res = myOperation.Run(sim).Result; ///... }

Dado que la clase QuantumSimulator implementa la interfaz IDisposable, debe llamar al método Dispose una vez que ya no necesite la instancia del simulador. La mejor manera de hacerlo es encapsular la declaración y las operaciones del simulador dentro de una instrucción using , que llama automáticamente al Dispose método .

Invocación del simulador desde Python

Use el método simulate() de la biblioteca de Python de Q# con la operación de Q# importada:

qubit_result = myOperation.simulate()

Invocación del simulador desde la línea de comandos

Cuando se ejecuta un Q# programa desde la línea de comandos, el simulador de estado completo es el equipo predeterminado target . Opcionalmente, puede usar el parámetro --simulator (o -s shortcut) para especificar la máquina deseada target . Los dos comandos siguientes ejecutan un programa mediante el simulador de estado completo.

dotnet run dotnet run -s QuantumSimulator

Invocación del simulador desde Jupyter Notebooks

Use el comando magic %simulate de IQ# para ejecutar la operación de Q#.

%simulate myOperation

Inicialización del simulador

De forma predeterminada, el simulador de estado completo usa un generador de números aleatorios para simular la aleatoriedad cuántica. Con fines de prueba, a veces resulta útil tener resultados deterministas. En un programa de C#, puede hacerlo proporcionando un valor de inicialización al generador de números aleatorios en el constructor QuantumSimulator con el parámetro randomNumberGeneratorSeed.

 using (var sim = new QuantumSimulator(randomNumberGeneratorSeed: 42)) { var res = myOperationTest.Run(sim).Result; ///... }

Opciones del simulador

El comportamiento del simulador de estado completo se puede ajustar al constructor de C# a través de los parámetros siguientes:

throwOnReleasingQubitsNotInZeroState
El simulador puede advertirle si los cúbits no se han devuelto al estado antes de liberarse zero iniciando una excepción. El valor predeterminado es true.

randomNumberGeneratorSeed
Obtenga el comportamiento determinista mediante la propagación del simulador.

disableBorrowing
Si no desea usar cúbits prestados para esta simulación, puede deshabilitar esta característica estableciendo este parámetro trueen . En su lugar, los cúbits prestados se reemplazarán por cúbits limpios normales. El valor predeterminado es false.

Nota

Restablecer o medir cúbits antes de que la especificación requiera la Q# versión; no hacerlo puede provocar errores computacionales.

El código siguiente muestra una posible configuración de los parámetros.

 var sim = new QuantumSimulator ( throwOnReleasingQubitsNotInZeroState: false, randomNumberGeneratorSeed: 42, disableBorrowing: true )

Configuración de subprocesos

El simulador de estado completo usa OpenMP para paralelizar el álgebra lineal requerida. De forma predeterminada, OpenMP usa todos los subprocesos de hardware disponibles, lo que significa que los programas con un número reducido de cúbits a menudo se ejecutan lentamente porque la coordinación necesaria enana el trabajo real. Puede corregirlo estableciendo la variable de entorno OMP_NUM_THREADS en un número pequeño. Como regla general, configure un subproceso para hasta cuatro cúbits y, a continuación, un subproceso adicional por cúbit. Es posible que tenga que ajustar la variable en función del algoritmo.

Consulte también

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Simulador cuántico de estado completo - Azure Quantum

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Obtenga información sobre cómo ejecutar Q# los programas en el simulador de estado completo de Microsoft Quantum Development Kit .

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https://grow-self.com/quantum-computing-programming-languages/

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Quantum computing is in its infancy, but, though still far from mainstream, it's quickly becoming more relevant than ever. Some experts believe that quantum computing will soon become the primary way we use computers as we know them today.

Disclaimer: This article is written for beginners who want to get into learning Quantum Computing, not for experienced professionals. If there are any mistakes in the content or you think it can be improved, please let us know in the comments below.

What exactly is a Quantum Computer?
It's a device made up of tiny particles (electrons) held together in a specific way by magnetic fields or lasers. These particles can be thought of as behaving like atoms with their own internal states and properties-but they're also capable of storing information about themselves even when they're not interacting with other objects or people!
Quantum computers are more powerful than classical computers because they can perform calculations that classical ones cannot. They do this by manipulating Qubits (quantum bits), which can be either 0 or 1 at any given moment and are essentially tiny versions of binary digits used in traditional computing systems.

A Qubit has two states: a Zero(0) state represented by a spin-up orientation and a One(1) state represented by a spin-down orientation. The two orientations represent different possible outcomes for any given calculation. This means that if you have one Qubit (quantum bit), then there are countless combinations possible for its state at any given time; hence why it can perform certain tasks faster than traditional computers powered by binary digits could ever dream about doing before.

Programming languages for quantum computing

Quantum computers are not widely available yet, but you can start learning to code in one now. Learning quantum computing programming languages can be challenging. The number of programming languages used regularly increases as the field of quantum computing grows.

🎯Also read: How to self-learn Quantum Computing?

While many different languages are being used, some are more widely used than others. Python is the most popular language for quantum computing and has been used in scientific projects since 2011. Qasm is a scripting language that uses Python syntax and features to create programs that can be run on any computer with no modifications required. C++ was also developed for use in quantum computing but lost popularity after it was found to have problems with error handling and lack of modularity compared to other languages such as Python or Haskell.

Haskell is another popular choice among developers due to its simplicity when compared with other programming paradigms like Java or C++ which makes it easy for new programmers who have little experience writing code before jumping into this world full force. without having any prior knowledge about what they're doing beforehand!

So, if you're interested in learning about quantum computing and its impact on the future of computing, programming languages are an excellent place to start. However, don't let the number of different languages intimidate you! There are only a handful of languages with recognized quantum computing implementations - all of which have their own strengths and weaknesses.
Let's take a closer look at some popular ones:
Python is a general-purpose programming language that can be used for many different tasks. It's also one of the most popular options for learning quantum computing because it's easy to learn and versatile enough to work with the quantum world.

Python has a large community of developers who provide support through online forums and meetups across the globe. This makes it easier to find help when you need it most-and if you're looking for more than just answers from someone else, there are plenty of tutorials on how best to use Python in your projects!

Qasm is a quantum assembly language and a high-level programming language for quantum computers. It can be used to program quantum computers and is built on top of the C++ programming language. The main goal of Qasm is to provide an easy-to-use language that allows you to write code in a declarative manner, which makes it much easier than writing classical assembly languages or machine code.

Qasm was created by scientists at Google Brain and Microsoft Research Redmond as part of their effort to develop new ways of programming their quantum computers (which we'll talk about later). As such, it has been made available under an open-source license so that anyone who wants access can do so-but don't worry if you're not interested in contributing back; it's still free!

C++ is a general-purpose programming language that was developed by Bjarne Stroustrup. It is used for many different types of applications, including games and video games. The main advantage of this language over other languages like Python or Java is its high level of abstraction, making it easier to write code in C++ than in any other language out there.

The syntax of C++ can be confusing at first because it uses some unique terms such as lambdas (functions) and objects (data structures). However, once you get used to them, they become second nature!

QCL is a functional programming language that's designed to be used on quantum computers. It's built to work with Qubits, which are quantum bits (qubits) of information that can store 0 and 1 at the same time. This allows them to perform sophisticated operations like addition, multiplication, and bitwise operators without losing any information about their state-a feature called "entanglement."

QCL was created by scientists at the University of Waterloo in Canada who wanted an easier way for programmers to write programs for their devices. It's been used since 2016 by researchers working on quantum computing systems; today, more than 100 people are working on it full-time!

5. Haskell and OCaml

Haskell and OCaml are both functional languages, which means that they're built around functions. Haskell has a much richer type system than most other programming languages-it lets you define a function as anything from an integer to a list of lists!

These two languages are used for quantum computing in the same way that Pascal and C were used for early computers. You can use them to build algorithms or simulate molecules; however, they aren't designed specifically for this purpose (although some people have done some amazing work using these tools). You can read about the differences between Haskell and OCaml here - Haskell v OCaml.

Q# (Q-Sharp) is a programming language designed specifically around quantum computing. It allows developers who still need to gain expertise in this area to quickly get started with coding algorithms that can be executed on real quantum hardware. Q# was released by Microsoft as an open-source, high-level programming language that can be used to write quantum algorithms at scale prioritizing execution time. Microsoft released an open-source project called Quantum Katas, which you can use to self-learn Q# at your own pace.

Qiskit was IBM's gift to the quantum programming world in 2017. An open-source Software Development Kit for working with quantum computers at the level of circuits, pulses, and algorithms, Qiskit was developed by IBM Research and the wider Qiskit community and provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Experience or on simulators on a local computer.

The wide use of quantum computers is just around the corner. If you want to be a part of the quantum computing bright future, take your pick from this list of languages to learn and dip your toes into the quantum computing universe.

Sources: https://thequantuminsider.com/2022/07/28/state-of-quantum-computing-programming-languages-in-2022/https://en.wikipedia.org/wiki/Quantum_programminghttps://research.aimultiple.com/quantum-computing-programming/https://www.amarchenkova.com/posts/programming-for-quantum-computinghttps://www.nature.com/articles/d41586-021-00533-xhttps://quantumcomputing.stackexchange.com/questions/1474/what-programming-languages-are-available-for-quantum-computers
Related articles: How To Self Learn Quantum Computing In 2023
9 Universities Offering Degree Programs In Quantum Computing
List Of The Best Quantum Computing Courses Online

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7 Quantum Computing Programming Languages to learn Quantum Computing - Grow-Self 🚀

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If you're interested in learning about quantum computing and its impact on the future of computing, quantum computing programming languages are an excellent place to start.

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Aquije Quiroga, Luis Enrique (u202114936)

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Aquije Quiroga, Luis Enrique (u202114936)

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http://repositorio.puce.edu.ec/bitstream/handle/22000/11156/Disertaci%c3%b3n%20Escrita_protected.pdf?sequence=1&isAllowed=y

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https://www.proydesa.org/portal/noticias/1708-lenguajes-de-programacion-cuanticos-un-futuro-proximo-que-empieza-a-asomar

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Las computadoras cuánticas todavía son bastante básicas y la mayoría son utilizadas como intrigantes juguetes en los laboratorios de investigación avanzada. Esto podría cambiar gracias a los nuevos lenguajes de programación que algunos expertos han empezado a desarrollar.
El más reciente proviene de Microsoft, que ha presentado su nuevo lenguaje Q# (pronunciado "Q sharp", haciendo referencia al popular lenguaje de programación C# de la compañía) junto a un conjunto de herramientas que ayudan a los desarrolladores a usarlo para crear software. Éste se suma a QCL y Quipper, los otros lenguajes de programación cuántica de alto nivel más conocidos.
Ahora bien, si prácticamente nadie tiene una computadora cuántica, ¿para qué sirven estos lenguajes de programación?

La gerente principal de investigación en computación cuántica en Microsoft, Krysta Svore, afirma que estos nuevos lenguajes son necesarios para sustituir a los que están escritos para los ordenadores actuales, que no funcionarán en las futuras computadoras cuánticas, ya que las computadoras clásicas codifican la información en forma binaria, con secuencias de unos y ceros, mientras que las computadoras cuánticas usan bits cuánticos, o "cúbits", que pueden codificar uno y cero al mismo tiempo.

Esto genera una gran cantidad de potencia de procesamiento paralelo y es el principal motivo por el que hay tanto interés en conseguir ordenadores cuánticos funcionales que ayuden a impulsar avances significativos en campos que van desde la ciencia de los materiales hasta la inteligencia artificial. Para explotar completamente ese poder o incluso encontrar los límites del mismo, los desarrolladores necesitarán estos lenguajes cuánticos para crear software que aproveche al máximo las capacidades de las computadoras.

Además, la complejidad del hardware cuántico hace imprescindible la existencia de lenguajes de programación de alto nivel que liberen a los desarrolladores de tener que entender cómo funciona exactamente un ordenador cuántico, al igual que ocurre en los lenguajes de programación para los ordenadores que utilizamos actualmente, que no requieren una comprensión profunda de cómo es la unidad central de procesamiento o CPU.

Otra de las peculiaridades de la computación cuántica es que tiene limitaciones que no existen en los lenguajes de programación clásicos. Por ejemplo, los programas cuánticos no pueden tener bucles que repitan una secuencia de instrucciones, sino que tienen que ejecutarse linealmente hasta su finalización. Para lidiar con estos problemas, Q# se combina con un par de lenguajes clásicos. Los desarrolladores sin experiencia cuántica pueden escribir así sus programas principales en lenguajes que ya conocen y luego usar un programa Q# cuando deseen involucrar el poder de procesamiento cuántico.

Aunque aún no posee una computadora cuántica propia, Microsoft ha lanzado varios simuladores que permiten a los desarrolladores probar programas escritos en Q# en ordenadores de sobremesa o en su servicio de computación en la nube Azure. IBM también ofrece simuladores y algunos desarrolladores afortunados pueden, incluso, ejecutar su código directamente en su máquina cuántica.

El experto en lenguaje de programación cuántica de la Universidad de Maryland, Xiaodi Wu, considera que los nuevos lenguajes cuánticos de alto nivel son el siguiente paso lógico. "Esto abrirá la puerta a que más personas usen estas máquinas", asegura, "lo que podría conducir a nuevos campos de investigación para la comunidad cuántica".

Ya se han realizado algunos llamamientos para crear nuevos lenguajes cuánticos de código abierto para que la comunidad de desarrolladores en general pueda ofrecer su aportación. El objetivo es darle a la computación cuántica el mismo tipo de impulso que el desarrollo de Linux le dio a internet.
Los desarrolladores de lenguajes de programación también tienen otro objetivo: los estudiantes que piensan en futuras carreras. Estos nuevos lenguajes que podrían hacer la informática cuántica más accesible deberían atraer a más personas al sector. "Queremos desarrollar la mano de obra cuántica", afirma Svore, "porque la informática cuántica abrirá una economía completamente nueva y vamos a necesitar personas que sean programadores cuánticos, desarrolladores de algoritmos e ingenieros".

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Lenguajes de Programación cuánticos: un futuro próximo que empieza a asomar

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Trabajamos y entregamos soluciones integrales de capacitación para abordar los complejos desafíos del mercado laboral IT. Certificaciones Cisco, Cursos Oracle.