This conference brings together researchers exploring the frontier where artificial intelligence meets quantum physics. As quantum simulation faces exponential complexity challenges, AI approaches offer exciting new possibilities. This conference examines how machine learning can represent quantum states, optimize quantum control protocols, and extract meaningful insights from experimental data. By uniting quantum physicists, computer scientists, and experimentalists, we aim to harness AI's capabilities to navigate high-dimensional Hilbert spaces, automate research tasks, and develop more effective frameworks for understanding quantum matter. Our discussions will bridge many directions such as neural network quantum representations, reinforcement learning for quantum control, and quantum learning theory, fostering interdisciplinary collaborations that push the boundaries of both fields.

This conference will hold a poster session all posters are welcome. Each poster board is 4 feet high x 6 feet wide. We ask that the posters be no larger than 44 inches high x 34 inches wide at the most (it is important to follow these measurements or posters may be turned down due to limited space). Feel free to submit your abstract