Photonic quantum computing

Photonic quantum computing has emerged as one of the most promising approaches to building scalable and fault-tolerant quantum processors. Among the companies advancing this vision, Quandela has taken a leading role by developing high-purity single-photon sources, integrated optical circuits, and a full software ecosystem for designing and simulating photonic quantum algorithms.

This course provides an introduction to the discrete-variable (DV) photonic computational model that underpins Quandela’s technology. In this paradigm, information is encoded in individual photons and manipulated through linear optical elements such as beamsplitters, phase shifters, and interferometers. The DV model contrasts with continuous-variable approaches, while offering strong compatibility with error-corrected architectures and measurement-based schemes.

We also explore the foundations of Perceval, Quandela’s open-source platform for photonic quantum programming. Perceval allows researchers and engineers to build optical circuits, simulate quantum protocols, benchmark algorithms, and interface with real photonic hardware. Understanding Perceval is essential for anyone wishing to design or experiment with photonic quantum algorithms, from Boson Sampling to quantum machine learning and advanced quantum communication protocols.

This material aims to offer both conceptual clarity and practical insight, bridging theoretical principles with the tools required to implement them.

This article presents a technical overview of my work with Quandela, focusing on the practical, algorithmic, and user-facing aspects of photonic quantum computing. As part of my preparation for the Support Engineer (Quantum Algorithms) position, I put together a structured set of notes and technical demonstrations that highlight both sides of the role:

1. the ability to understand and design photonic quantum algorithms using Perceval and Quandela’s hardware model,

2. the ability to support users by answering advanced technical questions on unitary decompositions, QASM conversion, QPU execution, and feed-forward configurations.

The first part of this document introduces a photonic quantum algorithm designed for generative modelling of calorimeter images—an application lying at the interface between high-energy physics and quantum photonics. It illustrates how a programmable interferometer, parameterised through a Clements decomposition, can be trained as a quantum generative model using Perceval.

The second part is a collection of user-ticket analyses and detailed explanations. These examples showcase how to diagnose user issues, clarify Perceval’s architecture (Circuits, Processors, RemoteProcessors), explain the limits of deterministic Clements decomposition, properly handle QASM conversion, and implement adaptive feed-forward logic using PNR detectors.

Taken together, these two components reflect the core responsibilities of a Support Engineer in photonic quantum algorithms:
bridging physics and software, translating user needs into concrete circuit implementations, and providing clear, rigorous, and pedagogical guidance on Quandela’s photonic quantum stack.