게이트 기반 양자 컴퓨팅
R2023a 이후
AWS®(Amazon® Web Services) 또는 IBM® Qiskit® Runtime Services를 사용하여 양자 게이트와 회로를 만들고, 로컬 컴퓨터에서 회로를 시뮬레이션하며, 원격 하드웨어에서 회로를 실행할 수 있습니다.
클래스
속성
| QuantumCircuitChart Properties | Quantum circuit plot appearance and behavior (R2023b 이후) |
함수
도움말 항목
기본 사항 및 워크플로
- Introduction to Quantum Computing
This topic contains background information about qubits, quantum gates, and quantum circuits. - 양자 게이트의 유형
이 항목에서는 양자 알고리즘을 작성하기 위한 여러 유형의 양자 게이트를 설명합니다. - Local Quantum State Simulation
This topic describes how to simulate a quantum circuit locally and analyze the simulation results. - 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 Service
This topic describes how to run quantum circuits on remote hardware using IBM Qiskit Runtime Service.
추천 예제
Graph Coloring with Grover's Algorithm
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)
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)
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
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.
