Out-of-Distribution Generalisation for Learning Quantum Channels with Low-Energy Coherent States

Learn about out-of-distribution generalization techniques for quantum channel learning using low-energy coherent states in this 47-minute conference talk presented by Pereira at the Galileo Galilei Institute. Explore advanced concepts in quantum machine learning that address how quantum systems can generalize beyond their training distributions when working with coherent states that have minimal energy requirements. Discover the theoretical foundations and practical implications of applying generalization principles to quantum channel characterization, examining how low-energy coherent states can be leveraged to improve learning performance in quantum information processing tasks. Gain insights into the intersection of quantum physics and machine learning theory, particularly focusing on the challenges and solutions for achieving robust quantum channel estimation when dealing with distribution shifts between training and testing scenarios.