Triangle Quantum Computing Seminar Series: Variational Quantum Boltzmann machines
Abstract: This work presents a novel realization approach to Quantum Boltzmann Machines (QBMs). The preparation of the required Gibbs states, as well as the evaluation of the loss function's analytic gradient is based on Variational Quantum Imaginary Time Evolution, a technique that is typically used for ground state computation. In contrast to existing methods, this implementation facilitates near-term compatible QBM training with gradients of the actual loss function for arbitrary parameterized Hamiltonians which do not necessarily have to be fully-visible but may also include hidden units. The variational Gibbs state approximation is demonstrated with numerical simulations and experiments run on real quantum hardware provided by IBM Quantum. Furthermore, we illustrate the application of this variational QBM approach to generative and discriminative learning tasks using numerical simulation.
Bio: Dr. Christa Zoufal is a Quantum Applications Researcher at IBM Quantum. She received her doctoral degree from ETH Zurich on her thesis about Generative Quantum Machine Learning. Her expertise combines knowledge about quantum information theory and computational methods. As part of the Quantum Finance & Optimization group her current research focuses on the exploitation of quantum computing within the context of quantum machine learning and optimization problems.
---Co-sponsors: IBM Quantum Hub at NC State, and Rethinc.Labs - Frank H. Kenan Institute of Private Enterprise at UNC - Chapel Hill