Quantum algorithms for classical causal learning

          @misc{ scivideos_PIRSA:24090089,
            doi = {10.48660/24090089},
            url = {https://pirsa.org/24090089},
            author = {Shrapnel, Sally},
            keywords = {Quantum Foundations, Quantum Information},
            language = {en},
            title = {Quantum algorithms for classical causal learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2024},
            month = {sep},
            note = {PIRSA:24090089 see, \url{https://scivideos.org/index.php/pirsa/24090089}}
          }
          

Abstract

Given the large number of proposed quantum machine learning (QML) algorithms, it is somewhat surprising that ideas from this field have not yet been extended to causal learning. While deep learning and generative machine learning models have taken centre stage in the industrial application of automated learning on classical data, it is nonetheless well known that these techniques don't reliably capture causal concepts, leading to significant performance vulnerabilities. Increasingly, classical ML experts are taking ideas from causal inference, a field traditionally limited to small data sets of low dimensionality, and injecting modern ML elements to create new algorithms that benefit from the best of both worlds. These hybrid classical approaches provide new opportunity to search for potential quantum advantage. In this talk I explore this new research direction and propose several new quantum algorithms for classical causal inference.