Quantum systems are inherently prone to noise and decoherence causing information loss. Given the enormous effort devoted to quantum technology and quantum computation, quantum control — i.e. how to preserve and manipulate quantum states — is a topic of great current interest and potential impact. Researchers have recently started to tap into the vast potential of machine learning (ML) to address many areas in physics, including quantum control and quantum error correction. The aim of the workshop is to build on and consolidate recent advances in using state-of-the-art ML techniques for the purpose of quantum control, quantum error correction, and methodologically related areas in condensed matter physics and beyond. The workshop will also address topics in quantum machine learning, such as quantum embeddings, quantum kernel methods and parametrised quantum circuits. It will bring together experts and pioneers in this rapidly developing field to further collaborations and raise the bar on machine learning applications for quantum physics. A special focus will be to engage researchers working on non-ML-related aspects of quantum computing and quantum technology in the Nordic region and beyond, to encourage cross-fertilization between the communities.