2021/03/31 by Julien Gacon, Christa Zoufal, Giuseppe Carleo +1 · 1 citation
Physics and Astronomy · #quant-ph
paper · pdf · doi:10.22331/q-2021-10-20-567
published as Quantum 5, 567 (2021)
arxiv created 2021/10/13 · arxiv updated 2021/10/20
The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computationally expensive and generally requires O(d2) function evaluations. To remedy these increasing costs in high-dimensional parameter spaces, we propose using simultaneous perturbation stochastic approximation techniques to approximate the QFIM at a constant cost. We present the resulting algorithm and successfully apply it to prepare Hamiltonian ground states and train Variational Quantum Boltzmann Machines.