The Graduate School Quantum Technologies of Université Paris Cité is organizing a one-day introductory session on quantum programming, led by Vivien Londe, a quantum computing specialist. The aim of the day is to implement well-known quantum algorithms using a quantum software development environment. No prior quantum programming is required, but basic knowledge of python and quantum information is recommended.

All Master's and PhD students, as well as postdocs and academics from Université Paris Cité and its QuanTech@Paris network (including Université Sorbonne Paris Nord and Inria Paris), are welcome to register.

1. Place: room 3071, IRIF 3rd floor, Université Paris Cité - Map
2. Date: May 15th, 2025 (9h-17h)
3. Instructor: Vivien Londe (Alice&Bob) - Blog
4. Materiel: Bring your laptop!
5. Registration is free but mandatory since attendance will be limited
6. Download the poster of the event

Quantum algorithms seek to be faster than classical ones. As such they are a generalization of quantum mechanics experiments which seek to showcase non classical behaviors. It is therefore not surprising that some quantum algorithms are directly inspired by physics experiment.

During the first part of the day, we will implement the phase estimation quantum algorithm. We will introduce quantum phase estimation as a generalization of interference experiments. A Mach-Zehnder interferometer experiment allows to estimate the refractive index of an unknown sample. Similarly, the quantum phase estimation algorithm allows to estimate the phase of an unknown quantum gate. The precision of the estimation of the refractive index is proportional to the length of the unknown sample. For the same reason, the precision of quantum phase estimation is proportional to the number of times the unknown quantum gate is applied.

No prior knowledge of a quantum programming language is required: the Q# code to implement the quantum phase estimation will be given. Basic knowledge of python and quantum information is recommended for the analysis of the precision. Since quantum phase estimation is the steppingstone towards Shor's algorithm and quantum algorithms for chemistry, we hope that attendees will obtain a solid understanding of this cornerstone of quantum algorithms.

Hamiltonian simulation is the use case for quantum computers originally envisioned by Feynmann: use a quantum system that we control well (the quantum computer) to simulate another quantum system that we want to investigate. The investigated quantum system is described by its Hamiltonian. Hamiltonian simulation opens the door to quantum chemistry and condensed matter applications. Today, forty years after Feynman's original insight, these applications are still among the most promising for quantum computing.

We will describe two techniques for Hamiltonian simulation: Trotterization and quantum signal processing on a block-encoded Hamiltonian. We will implement some subroutines in Q#. We will see how Hamiltonian simulation combined with phase estimation allows to estimate an energy level of a quantum state. Energy estimation is one of the main applications of quantum computing in quantum chemistry and condensed matter.

1. Place: room 3071, IRIF 3rd floor, Université Paris Cité - Map
2. Date: April 26th, 2024 (9h-17h)
3. Instructor: Vivien Londe (Microsoft) - Blog
4. Materiel: Bring your laptop!
5. Registration is free but mandatory since attendance will be limited (close)
6. Download the poster of the event

Quantum algorithms seek to be faster than classical ones. As such they are a generalization of quantum mechanics experiments which seek to showcase non classical behaviors. It is therefore not surprising that some quantum algorithms are directly inspired by physics experiment.

During the first part of the day, we will implement the phase estimation quantum algorithm. We will introduce quantum phase estimation as a generalization of interference experiments. A Mach-Zehnder interferometer experiment allows to estimate the refractive index of an unknown sample. Similarly, the quantum phase estimation algorithm allows to estimate the phase of an unknown quantum gate. The precision of the estimation of the refractive index is proportional to the length of the unknown sample. For the same reason, the precision of quantum phase estimation is proportional to the number of times the unknown quantum gate is applied.

No prior knowledge of a quantum programming language is required: the Q# code to implement the quantum phase estimation will be given. Basic knowledge of python is recommended for the analysis of the precision. Since quantum phase estimation is the steppingstone towards Shor's algorithm and quantum algorithms for chemistry, we hope that attendees will obtain a solid understanding of this cornerstone of quantum algorithms.

The major obstacle to running quantum algorithms for large problems is the fragility of quantum information. Decoherence and other sources of error make it extremely difficult to obtain an error-free output from a large quantum program. Thankfully, theoretical answers to the problem of quantum errors are known.

During the second part, we will explore the most famous quantum error correction code: the surface code. After a short introduction to the theory of error correction and to the surface code, we will dive into its numerical implementation. We will explore numerically a decoder for the erasure channel and mention extensions to the depolarizing channel. Basic programming skills in a classical language (for instance in python) is sufficient for this part.