Quantum machine learning asks whether quantum computers can offer machine learning capabilities beyond classical methods. Assessing this potential requires a firm understanding of its fundamental concepts, the basic models, and their limitations. This talk reviews these foundations and the challenges of working with quantum devices before focusing on supervised quantum machine learning with...
Nonlocal games provide a framework for studying cooperative strategies under different resource assumptions. These range from independent agents with no shared resource, through local hidden variable (LHV), to entangled quantum states, allowing one to quantify the advantage of quantum strategies over classical ones. Discovering optimal quantum strategies, however, remains a non-trivial task....
Detecting statistical dependence between stochastic processes is a core primitive of causal discovery for dynamical systems. State-of-the-art tests compare whole trajectories with the signature kernel, which propagates a static point-similarity kernel through a Goursat PDE and feeds the resulting Gram matrices into kernel independence tests (HSIC, SDCIT). We investigate replacing the classical...
