Quantum amplitude estimation is a key subroutine in a number of powerful
quantum algorithms, including quantum-enhanced Monte Carlo simulation and
quantum machine learning. Maximum-likelihood quantum amplitude estimation
(MLQAE) is one of a number of recent approaches that employ much simpler
quantum circuits than the original algorithm based on quantum phase estimation.
In this article, we deepen the analysis of MLQAE to put the algorithm in a more
prescriptive form, including scenarios where quantum circuit depth is limited.
In the process, we observe and explain particular ranges of `exceptional'
values of the target amplitude for which the algorithm fails to achieve the
desired precision. We then propose and numerically validate a heuristic
modification to the algorithm to overcome this problem, bringing the algorithm
even closer to being useful as a practical subroutine on near- and mid-term
quantum hardware.Comment: 22+2 pages, 10 figure