### 物理代写|量子计算代写Quantum computer代考|Touring the IBM Quantum® Hardware with Qiskit®

statistics-lab™ 为您的留学生涯保驾护航 在代写量子计算Quantum computer方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写量子计算Quantum computer代写方面经验极为丰富，各种代写量子计算Quantum computer相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 物理代写|量子计算代写Quantum computer代考|What are the IBM Quantum® machines

This section is less of a recipe and rather more of a basic overview of the quantum components and processes that you will be encountering. If you’d rather jump ahead and start coding right away, then go to the next recipe.

With Qiskit”, you can run your quantum programs on two types of quantum computers: simulators and IBM Quantum” hardware. The simulators run either locally or in the cloud on IBM hardware. Generally speaking, running a simulator in the cloud gives you greater power and performance; ibmq_gasm_simulator – available online – lets you run fairly deep quantum programs on up to 32 qubits. Your local simulator performance depends on your hardware; remember that simulating a quantum computer gets exponentially harder with each qubit added.The actual IBM quantum computer hardware is located in an IBM lab and is accessed through the cloud. There are good reasons for this, so let’s walk through this recipe on how to set up and run a quantum computer with the superconducting qubits that IBM Quantum ${ }^{*}$ provides.

Superconducting quantum computers are extremely sensitive to noise such as electromagnetic radiation, sound waves, and heat. An isolated environment equipped with cryogenic cooling provides a location with as little disturbance as possible.
Quantum computers may use so-called Josephson junctions kept at cryogenic temperatures and manipulated by microwave pulses. Ordinary people do not possess this kind of hardware, so, in this book, we will use the freely available IBM quantum computers in the cloud for our quantum programming.

## 物理代写|量子计算代写Quantum computer代考|Locating the available backends

In Qiskit”, a backend represents the system on which you run your quantum program. A backend can be a simulator, like the local Aer simulator that we have used earlier. If you want to run your quantum programs on real quantum computers instead of on your local simulator, you must identify an IBM Quantum” machine as a backend to use, and then configure your quantum program to use it.
Let’s see the steps of what we’ll be doing:

1. Start by importing the required classes and methods and load your account information. In this case, we use the IBMQ class, which contains the main hardware-related functions.
2. Take a look at the machines that are available to your account.
3. Select a generally available backend.
4. Create and run a Bell state quantum program on the selected backend.
5. Select a simulator backend and run the Bell state quantum program again for comparison.
In this recipe, we will use the IBMQ provider .backends () method to identify and filter available backends to run your programs and then use the provider.get backend () method to select the backend. In the example that follows, we will use the ibmqx2 and ibmq_qasm_simulator backends. We will then run a small quantum program on one of the hardware backends, and then on the simulator backend.
The Python file in the following recipe can be downloaded from here: https : // github. com/PacktPublishing/Quantum-Computing-in-Practice-withQiskit-and-IBM-Quantum-Experience/blob/master/Chapter05/ch5_ r1_identifying_backends.py .

## 物理代写|量子计算代写Quantum computer代考|Comparing backends

The IBM Quantum” backends are all slightly different, from the number of qubits to the individual behavior and interaction between these. Depending on how you write your quantum program, you might want to run the code on a machine with certain characteristics.
The backend information that is returned by IBMQ is just a plain Python list and you can juggle the returned data with any other list. For example, you can write a Python script that finds the available IBM Quantum” backends, then run a quantum program on each of the backends and compare the results in a diagram that shows a rough measure of the quality of the backends’ qubits.

In this recipe, we will use a simple Python loop to run a succession of identical Bell-state quantum programs on the available IBM Quantum” backends to get a rough estimate of the performance of the backends.
The file required for this recipe can be downloaded from here: https://github .com/ PacktPublishing/Quantum-Computing-in-Practice-with-Qiskit-andIBM-Quantum-Experience/blob/master/Chapter05/ch5_r2_comparing_ backends . py.

## 物理代写|量子计算代写Quantum computer代考|Locating the available backends

1. 首先导入所需的类和方法并加载您的帐户信息。在这种情况下，我们使用 IBMQ 类，其中包含主要的硬件相关功能。
2. 查看您的帐户可用的机器。
3. 选择一个普遍可用的后端。
4. 在选定的后端创建并运行贝尔状态量子程序。
5. 选择一个模拟器后端，再次运行贝尔状态量子程序进行比较。
准备
在这个秘籍中，我们将使用 IBMQ 提供者 .backends () 方法来识别和过滤可用的后端来运行您的程序，然后使用 provider.get backend () 方法来选择后端。在下面的示例中，我们将使用 ibmqx2 和 ibmq_qasm_simulator 后端。然后，我们将在其中一个硬件后端上运行一个小型量子程序，然后在模拟器后端上运行。
以下配方中的 Python 文件可以从这里下载：https : // github。com/PacktPublishing/Quantum-Computing-in-Practice-withQiskit-and-IBM-Quantum-Experience/blob/master/Chapter05/ch5_r1_identifying_backends.py。

## 物理代写|量子计算代写Quantum computer代考|Comparing backends

IBM Quantum 的后端都略有不同，从量子比特的数量到个体行为和它们之间的交互。根据您编写量子程序的方式，您可能希望在具有某些特征的机器上运行代码。
IBMQ 返回的后端信息只是一个普通的 Python 列表，您可以将返回的数据与任何其他列表混合使用。例如，您可以编写一个 Python 脚本来查找可用的 IBM Quantum 后端，然后在每个后端上运行一个量子程序，并在图表中比较结果，该图表显示了后端量子比特质量的粗略测量。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。