The Graduate School Quantum Technologies is organizing a one-day session of introduction to quantum programming by Vivien Londe, Quantum computing specialist at Microsoft. All Master and PhD students, as well as postdocs and academics of Université Paris Cité are welcome to register. The scope of the day is to implement well known quantum algorithms in an environment for quantum software development.

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
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.