  We present qLEET, an open-source Python package for studying parameterized
quantum circuits (PQCs), which are widely used in various variational quantum
algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables
the computation of properties such as expressibility and entangling power of a
PQC by studying its entanglement spectrum and the distribution of parameterized
states produced by it. Furthermore, it allows users to visualize the training
trajectories of PQCs along with high-dimensional loss landscapes generated by
them for different objective functions. It supports quantum circuits and noise
models built using popular quantum computing libraries such as Qiskit, Cirq,
and Pyquil. In our work, we demonstrate how qLEET provides opportunities to
design and improve hybrid quantum-classical algorithms by utilizing intuitive
insights from the ansatz capability and structure of the loss landscape.
