This device is not compatible.


Evaluate Quantum Fourier Transform Using Quantum Machine Learning

In this project, we will evaluate the Quantum Fourier Transform of a statevector through a variational quantum circuit. A gradient-based optimization approach is used with the help of the PennyLane library.

Evaluate Quantum Fourier Transform Using Quantum Machine Learning

You will learn to:

Plot simulation results in Python.

Optimize a quantum circuit with dedicated packages.

Optimize a quantum circuit using a machine learning approach.

Create and simulate a quantum circuit using PennyLane.


Quantum Computing

Quantum Machine Learning



Basic understanding of the Python language

Basic knowledge of linear algebra concepts

Basic understanding of quantum computing concepts

Basic understanding of machine learning concepts

Basic understanding of the optimization theory





Project Description

Quantum machine learning is a subdomain of quantum computing in which machine learning is integrated with quantum algorithms for data analysis on a quantum computer. Quantum machine learning provides an enhanced, and, in certain cases, improved performance over classical machine learning algorithms.

Quantum Fourier transform is a quantum operator that evaluates the Fourier transform of a state vector. The unitary matrix representation of this operator is as follows:

[1111eι2πNeι2π(N1)N1eι2π.2Neι2π.2(N1)N1eι2π.3Neι2π.3(N1)N1eι2π.(N1)Neι2π.(N1)(N1)N]\begin{bmatrix} 1 & 1 & \dots & 1\\ 1 & e^{\frac{\iota 2\pi}{N}} & \dots & e^{\frac{\iota2\pi(N-1)}{N}}\\ 1 & e^{\frac{\iota 2\pi. 2}{N}} & \dots & e^{\frac{\iota2\pi. 2(N-1)}{N}}\\ 1 & e^{\frac{\iota 2\pi. 3}{N}} & \dots & e^{\frac{\iota2\pi.3(N-1)}{N}}\\ \vdots & \vdots & \dots & \vdots \\ 1 & e^{\frac{\iota 2\pi.(N-1)}{N}} & \dots & e^{\frac{\iota2\pi.(N-1)(N-1)}{N}}\\ \end{bmatrix}

Here, NN is the number of qubits in the quantum state.

In this project, we’ll train a variational quantum circuitA variational quantum circuit, also called a parameterized quantum circuit, is a circuit with variable parameters. using quantum machine learning. This will provide hands-on experience constructing and training a quantum circuit using a machine learning approach.

Project Tasks


Construct the Quantum Circuit

Task 0: Introduction

Task 1: Import Libraries

Task 2: Load a Quantum Device

Task 3: Create the Quantum Circuit

Task 4: Apply the Inverse QFT Matrix

Task 5: Add the Measurement Gates

Task 6: Create the Quantum Node


Optimize the Quantum Circuit

Task 7: Create the Cost Function

Task 8: Initialize the Optimizer

Task 9: Construct the Optimization Block

Task 10: Test the Optimizer

Task 11: Visualize the Optimization Process