Reinforcement learning using quantum Boltzmann machines
(pp0051-0074)
Daniel
Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, and Pooya
Ronagh
doi:
https://doi.org/10.26421/QIC18.1-2-3
Abstracts:
We investigate whether quantum annealers with select chip
layouts can outperform classical computers in reinforcement learning
tasks. We associate a transverse field Ising spin Hamiltonian with a
layout of qubits similar to that of a deep Boltzmann machine (DBM) and
use simulated quantum annealing (SQA) to numerically simulate quantum
sampling from this system. We design a reinforcement learning algorithm
in which the set of visible nodes representing the states and actions of
an optimal policy are the first and last layers of the deep network. In
absence of a transverse field, our simulations show that DBMs are
trained more effectively than restricted Boltzmann machines (RBM) with
the same number of nodes. We then develop a framework for training the
network as a quantum Boltzmann machine (QBM) in the presence of a
significant transverse field for reinforcement learning. This method
also outperforms the reinforcement learning method that uses RBMs.
Key words:
Reinforcement learning, Machine learning, Neuro-dynamic
programming, Markov decision process, Quantum Monte Carlo simulation,
Simulated quantum annealing, Restricted Boltzmann machine, Deep
Boltzmann machine, General Boltzmann machine, Quantum Boltzmann machine
Communicated by: S Braunstein & A Harr |