Machine Learning & Optimal Control Internship H/F

Overview

Alice & Bob is developing the first universal, fault-tolerant quantum computer to solve the world’s hardest problems. The quantum computer we envision building is based on a new kind of superconducting qubit: the Schrödinger cat qubit. In comparison to other superconducting platforms, cat qubits have the astonishing ability to implement quantum error correction autonomously!

Team and Role

The Performance Optimization Team is at the heart of our mission: improving the control, speed, and reliability of our unique superconducting cat-qubit architecture through a powerful combination of physical modeling, optimization, and data-driven methods.

As an Optimal Control Intern, you will serve as the vital bridge between exploratory ML research and physical quantum experiments.

Responsibilities

• Advance optimal control strategies: develop methods to improve the optimization of quantum gates and state-preparation protocols under realistic noise, dissipation, and hardware constraints.
• Explore RL‑based control approaches: formulate selected adaptive control or sequential experiment‑design tasks as reinforcement learning problems and evaluate when RL offers benefits over more structured physics‑based methods.
• Design adaptive experiments: build strategies that use measurement history and model uncertainty to choose the next most informative experiment or control setting.
• Study parameter sensitivity and identifiability: leverage differentiable simulators and open-system models to understand which experiments best constrain key physical parameters.
• Support hardware integration: account for practical constraints such as inference speed, transfer latency, and compilation time when designing methods for laboratory use.
• Cross‑functional collaboration: partner closely with physicists and ML researchers to interpret experimental data and translate complex physical requirements into robust software solutions.

Requirements

• Currently pursuing a Master’s degree in Physics, Machine Learning, Applied Mathematics, or a closely related field (seeking a 5‑6 month internship).
• Strong academic background in physics, optimization, mathematical modeling, or control.
• Fluency in English (both written and spoken).
• Strong proficiency in Python programming.
• Familiarity with modern tensor libraries such as PyTorch or JAX, and/or quantum frameworks such as Qiskit or Dynamiqs.
• Experience working with open quantum systems, quantum optics, or superconducting circuits.
• Interest in optimal control, reinforcement learning, parameter estimation or adaptive experiment design.
• Experience training models or running large-scale simulations on GPU clusters is a plus.
• A proactive, curious mindset with a desire to test algorithms on real‑world, noisy hardware rather than just relying on ideal simulations.

Benefits

• 1 day off per month
• Half of transportation cost coverage (as per French law)
• Meal vouchers with Swile, as well as access to a fully equipped and regularly stocked kitchen

We encourage applicants of all backgrounds to apply.