landscape-tools 0.0.4

Internal tools for landscape characterization

Landscape Tools

Installation

pip install landscape_tools

from landscape_tools import landscape_visualization as lv
from landscape_tools import barren_plateaus as bp

Landscape Characterization

This module provides utilities to analyze and visualize the optimization landscape of variational quantum algorithms (VQAs).

It is designed to work independently of QUICK / HDES internals and can be used with any backend, as long as a suitable cost function is provided.

The main purpose of this module is to:

• perform 1D and 2D loss landscape scans,
• analyze loss landscapes in PCA subspaces,
• study gradient magnitudes and barren plateau effects,
• visualize trajectories and parameter influence.

Most algorithms implemented here only depend on a generic cost function interface, not on circuits, observables, or backends.

All landscape analysis functions expect a cost function of the form:

f(theta: np.ndarray) -> float

In practice, you must wrap the original cost function so that it only takes the parameter vector as input and returns a scalar value.

def your_cost_function(params, circuit, hamiltonian):
    state = circuit.run(params)
    return expectation_value(state, hamiltonian)

wrapped_loss = lambda p: cost_function(
    p,
    circuit=my_circuit,
    hamiltonian=my_hamiltonian,
)