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!
We're a diverse team of 140+ brilliant minds from over 20 countries united by a single goal: to revolutionise computing with a practical fault-tolerant quantum machine. Are you ready to take on unprecedented challenges and contribute to revolutionising technology? Join us, and let's shape the future of quantum computing together!
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. You will focus on action. Working directly alongside ML scientists, quantum device theorists, and lab experimentalists, you will combine quantum device theory with physics-informed machine learning and reinforcement learning. Your primary goal is to develop physically grounded strategies to improve control, focusing heavily on adaptive experiment design, parameter sensitivity, uncertainty reduction, and pulse optimization.
Your work will center on open-system quantum dynamics under realistic hardware constraints. Utilizing differentiable simulators and physics-informed methods, you will design efficient control and measurement strategies, accelerate the optimization of quantum operations, and identify the most informative experiments for characterizing device behavior.