  The paper proposes a new Monte-Carlo simulator combining the advantages of
Sequential Monte Carlo simulators and Hamiltonian Monte Carlo simulators. The
result is a method that is robust to multimodality and complex shapes to use
for inference in presence of difficult likelihoods or target functions. Several
examples are provided.
