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ゲートベースの量子計算
ゲートベースの量子計算アルゴリズム
R2023a 以降
R2023a 以降
量子ゲートおよび回路を作成し、ローカル コンピューターで回路をシミュレーションし、Amazon® Web Services (AWS®) または IBM® Qiskit® Runtime Services を使用してリモート ハードウェアで回路を実行します。
クラス
プロパティ
QuantumCircuitChart Properties | Quantum circuit plot appearance and behavior (R2023b 以降) |
関数
トピック
基本とワークフロー
- Introduction to Quantum Computing
This topic contains background information about qubits, quantum gates, and quantum circuits. - 量子ゲートのタイプ
このトピックでは、量子アルゴリズムを作成するための量子ゲートのさまざまなタイプについて説明します。 - ローカル量子状態シミュレーション
このトピックでは、量子回路をローカルでシミューレートしてシミュレーション結果を解析する方法について説明します。 - Run Quantum Circuit on Hardware Using AWS
This topic describes how to run quantum circuits on remote hardware using Amazon Web Services. - Run Quantum Circuit on Hardware Using IBM Qiskit Runtime Services
This topic describes how to run quantum circuits on remote hardware using IBM Qiskit® Runtime Services.
適用
- Graph Coloring with Grover's Algorithm
This example shows how to use Grover's algorithm on a quantum computer to solve graph coloring problems. Grover's algorithm, also called the quantum search algorithm, is a fast method to perform unstructured searches. This example applies the algorithm to a problem where a bit string of a given length is classified as valid or invalid, and the goal is to retrieve one of the valid bit strings. The algorithm uses a state oracle to determine whether a bit string is valid. Although this application of Grover's algorithm is not the most efficient method to solve the graph coloring problem in practice, it illustrates how a quantum algorithm can be applied to a well-known problem. - Ground-State Protein Folding Using Variational Quantum Eigensolver (VQE)
This example shows an efficient method for using qubits to encode a protein fold on a 3-D tetrahedral lattice [1], [2]. The ground-state is found through a simulated variational quantum eigensolver (VQE) routine. The VQE algorithm uses classical optimization to improve the initial guess of the ground state, and then a quantum computer calculates the expectation value. The final circuit from the simulation is run on a real QPU for comparison. - Solve XOR Problem Using Quantum Neural Network (QNN)
This example shows how to solve the XOR problem using a trained quantum neural network (QNN). You use the network to classify the classical data of 2-D coordinates. A QNN is a machine learning model that combines quantum computing layers and classical layers. This example shows how to train such a hybrid network for a classification problem that is nonlinearly separable, such as the exclusive-OR (XOR) problem. - Quantum Monte Carlo (QMC) Simulation
This example shows how to use Quantum Monte Carlo (QMC) simulation in MATLAB® to compute the mean of a function of a random variable. There are a broad range of tasks in finance and economics that depend on Monte Carlo simulation, from option pricing to macroeconomic stress testing. While this example does not explore computational efficiency, research shows that QMC offers a quadratic speed-up compared to classic Monte Carlo methods.