A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning
Joseph Lindsay Ramtin Zand
Works in quantum machine learning (QML) over the past few years indicate that
QML algorithms can function just as well as their classical counterparts, and
even outperform them in some cases. Among the corpus of recent work, many
current QML models take advantage of variational quantum algorithm (VQA)
circuits, given that their scale is typically small enough to be compatible
with NISQ devices and the method of automatic differentiation for optimizing
circuit parameters is familiar to machine learning (ML). While the results bear
interesting promise for an era when quantum machines are more readily
accessible, if one can achieve similar results through non-quantum methods then
there may be a more near-term advantage available to practitioners. To this
end, the nature of this work is to investigate the utilization of stochastic
methods inspired by a variational quantum version of the long short-term memory
(LSTM) model in an attempt to approach the reported successes in performance
and rapid convergence. By analyzing the performance of classical, stochastic,
and quantum methods, this work aims to elucidate if it is possible to achieve
some of QML's major reported benefits on classical machines by incorporating
aspects of its stochasticity.