Photonic Quantum Computing Tutorials

Overview

This repository contains practical tutorials demonstrating the use of Quandela’s photonic quantum computing technology. The material is designed to provide hands-on examples ranging from foundational photonic circuit simulations to quantum machine learning workflows.

The tutorials are organised into two main areas:

• Photonics-Perceval — Tutorials using the Perceval library for modelling and simulating photonic quantum circuits.
• Quantum_Machine_Learning-MerLin — Tutorials combining MerLin and PyTorch for quantum machine learning applications built on photonic quantum computing principles.

Official MerLin documentation: https://merlinquantum.ai/

Official Perceval documentation: https://perceval.quandela.net/

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Repository Structure

Photonic-Quantum-Computing-Tutorials/
│
├── Photonics_with_Perceval/
│   └── Tutorials using the Perceval library
│
├── QML_with_MerLin/
│   └── Tutorials using MerLin and PyTorch
│
├── img/
│   └── Images and figures used throughout the tutorials
│
└── requirements.txt

Photonics-Perceval

This folder contains tutorials focused on:

• Building and simulating photonic quantum circuits
• Modelling linear optical components
• Running simulations using Perceval
• Exploring photonic quantum computation concepts in practice

Quantum_Machine_Learning-MerLin

This folder contains tutorials covering:

• Integration of MerLin with PyTorch
• Hybrid quantum-classical workflows
• Photonic quantum models for machine learning tasks
• Training and evaluating quantum-enhanced models

img

This directory contains images, diagrams, and visual assets referenced within the tutorials.

requirements.txt

This file lists the Python dependencies required to run the tutorials. It ensures a reproducible environment across systems.

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Installation

It is recommended to use a virtual environment.

macOS / Linux

Create a virtual environment:

python3 -m venv venv

Activate it:

source venv/bin/activate

Windows (PowerShell)

Create a virtual environment:

python -m venv venv

Activate it:

venv\Scripts\Activate.ps1

Windows (Command Prompt)

Create a virtual environment:

python -m venv venv

Activate it:

venv\Scripts\activate.bat

Install the required dependencies:

pip install -r requirements.txt

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Using a .env File for Cloud Access

Some tutorials require access to Quandela Cloud services via a Cloud Token. To avoid hard-coding credentials, the token is stored in a .env file.

1. Create a .env File

In the root of the repository, create a file named:

.env

Add your Cloud Token:

QUANDELA_CLOUD_TOKEN=your_token_here

2. Load the Environment Variables

If required, install python-dotenv:

pip install python-dotenv

The tutorials load the token from the .env file at runtime, making it available as an environment variable.

Important

• Do not commit the .env file to version control.
• Keep your Cloud Token confidential.
• Ensure .env is listed in your .gitignore.

Prerequisites

• Python 3.9 or newer (unless otherwise specified in requirements.txt)
• Basic familiarity with Python
• Introductory knowledge of quantum computing concepts
• PyTorch familiarity for the quantum machine learning tutorials

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Usage

Navigate to the relevant tutorial folder and follow the instructions provided within each notebook or script.

For example:

cd Photonics-Perceval

or

cd Quantum_Machine_Learning-MerLin

Open the tutorials in Jupyter Notebook or JupyterLab:

jupyter notebook

or

jupyter lab

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Purpose

This repository is intended for:

• Researchers exploring photonic quantum computing
• Developers building applications with Quandela’s technology
• Students learning practical quantum photonics
• Practitioners interested in quantum machine learning with photonic systems

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Licence

Please refer to the appropriate licence terms associated with the included libraries (Perceval, MerLin) and any additional project-specific licensing information.