Learning to Encode and Decode Quantum Information: Neural Network Approaches to Quantum Error Correction
Quantum computing and artificial intelligence represent two of the most transformative technological paradigms of our era, each poised to redefine the boundaries of scientific discovery. A growing body of research has explored whether these technologies can be synergistically combined. In this talk, I will examine the application of classical AI algorithms to quantum error correction. AI-driven approaches offer a promising avenue for optimising error correction protocols to the device-specific noise patterns of real quantum hardware. I will present our recent work on neural post-selection and learning-based optimisation algorithms to discover good quantum error correction codes, explaining how exploiting the mathematical structure of quantum codes in the AI algorithms design can improve the learning process. I will conclude with a forward-looking discussion on the path towards scalable, AI-driven quantum error correction.