  Variational Quantum Algorithms (VQAs) are promising candidates for finding
practical applications of near to mid-term quantum computers. There has been an
increasing effort to study the intricacies of VQAs, such as the presence or
absence of barren plateaus and the design of good quantum circuit ans\"atze.
Many of these studies can be linked to the loss landscape that is optimized as
part of the algorithm, and there is high demand for quality software tools for
flexibly studying these loss landscapes. In our work, we collect a variety of
techniques that have been used to visualize the training of deep artificial
neural networks and apply them to visualize the high-dimensional loss
landscapes of VQAs. We review and apply the techniques to three types of VQAs:
the Quantum Approximate Optimization Algorithm, the Quantum Circuit Born
Machine, and the Variational Quantum Eigensolver. Additionally, we investigate
the impact of noise due to finite sampling in the estimation of loss functions.
For each case, we demonstrate how our visualization techniques can verify
observations from past studies and provide new insights. This work is
accompanied by the release of the open-source Python package $\textit{orqviz}$,
which provides code to compute and flexibly plot 1D and 2D scans, Principal
Component Analysis scans, Hessians, and the Nudged Elastic Band algorithm.
$\textit{orqviz}$ enables flexible visual analysis of high-dimensional VQA
landscapes and can be found at: $\textbf{github.com/zapatacomputing/orqviz}$.
