Researchers have developed an innovative method to control quantum systems through autonomous Floquet engineering, utilizing reinforcement learning. This approach enables the manipulation of bosonic codes, a promising architecture for fault-tolerant quantum computing, without the need for constant human supervision. Floquet engineering involves the periodic application of pulses to a quantum system to induce new properties or protect quantum states, and its automation is a crucial step towards the scalability of quantum computers.

Reinforcement learning, a branch of artificial intelligence, is used for an AI agent to learn how to optimize the sequence and parameters of Floquet pulses. The agent interacts with a model of the quantum system, receiving rewards for actions that lead to desired quantum states or increased coherence. This iterative process allows the algorithm to discover control strategies that might be difficult to design manually, especially in complex systems with many degrees of freedom.

The application of this technique to bosonic codes is particularly relevant. These codes encode quantum information in the different states of a quantum harmonic oscillator, offering advantages in error protection. The ability to autonomously apply Floquet engineering to these codes could significantly enhance their robustness and reliability, bringing closer the realization of a quantum computer capable of overcoming current limitations of decoherence and errors.