Tameem Albash

Parametrized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work, we focus on the role played by the temperature distribution of the parallel tempering replicas.

We study how decoherence increases the efficiency with which we can simulate the quantum dynamics of an anharmonic oscillator governed by the Kerr effect. As decoherence washes out the fine-grained sub-Planck structure associated with phase-space quantum interference in the closed quantum system, open quantum dynamics can be more efficiently simulated using a coarse-grained finite-difference numerical integration.

We consider the transverse Ising model with a time-dependent Hamiltonian of the form: where t is the total annealing time, s = t/t  ∈ [0, 1], and A(s) and B(s) are the annealing schedules, monotonically decreasing and increasing, respectively, satisfying B(0) = 0 and A(1) = 0.