Quantum Boltzmann Machine using a Quantum Annealer

          @misc{ scivideos_PIRSA:16080009,
            doi = {10.48660/16080009},
            url = {https://pirsa.org/16080009},
            author = {Amin, Mohammad},
            keywords = {Quantum Matter},
            language = {en},
            title = {Quantum Boltzmann Machine using a Quantum Annealer},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2016},
            month = {aug},
            note = {PIRSA:16080009 see, \url{https://scivideos.org/index.php/pirsa/16080009}}
          }
          

Abstract

Machine learning is a rapidly growing field in computer science with applications in computer vision, voice recognition, medical diagnosis, spam filtering, search engines, etc. In this presentation, I will introduce a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Model. Due to the non-commutative nature of quantum mechanics, the training process of the Quantum Boltzmann Machine (QBM) can become nontrivial.  I will show how to circumvent this problem by introducing bounds on the quantum probabilities. This allows training the QBM efficiently by sampling. I will then show examples of QBM training with and without the bound, using exact diagonalization, and compare the results with classical Boltzmann training. Finally, after a brief introduction to D-Wave quantum annealing processors, I will discuss the possibility of using such processors for QBM training and application.