Quick Start

High-Level Interface (Recommended)

The easiest way to get started is with the convenience interface:

from qutree import optimize_function

# Define your function with named parameters
def rosenbrock(x, y):
    return (1 - x)**2 + 100*(y - x**2)**2

# Define parameter bounds
bounds = {'x': (-2, 2), 'y': (-1, 3)}

# Optimize!
result = optimize_function(rosenbrock, bounds)

print(f"Optimal x={result['x']['x']:.3f}, y={result['x']['y']:.3f}")
print(f"Minimum value: {result['fun']:.6f}")
print(f"Function evaluations: {result['n_calls']}")

That’s it! The convenience interface handles all the tensor network details for you.

See Convenience Interface for more examples and advanced features like:

• Per-parameter grid points

• Warm-start optimization

• Different function signatures

• Hyperparameter tuning

Low-Level Interface

For more control over the tensor network structure, use the low-level API:

from qutree import *
import numpy as np

# Define objective function
def V(x):
    point = np.array(list(range(x.shape[0])))
    return np.sum((x - point)**2)

# Create objective wrapper
objective = Objective(V)

# Parameters
N = 21      # Grid points per dimension
r = 4       # Bond dimension
f = 3       # Number of features/dimensions
nsweeps = 3 # Number of optimization sweeps

# Create tensor train graph
G = tensor_train_graph(f, r, N)

# Define primitive grid boundaries
primitive_grid = [np.linspace(0., 4., num=N)] * f

# Run optimization
G_opt = ttnopt(G, objective, nsweeps, primitive_grid)

# Access results
print(objective)
print(objective.logger.df)

The optimization will find the minimum of the function V(x), which is at x = [0, 1, 2].

Visualizing the Network

You can visualize the tensor network structure:

from qutree import plot_tt_diagram, plot_tree

# For tensor train
fig = plot_tt_diagram(G)

# For tree structures
fig = plot_tree(G)

Next Steps

• Start with the Convenience Interface for easy optimization

• Learn about different tree structures

• Explore TTNOpt optimization in detail for low-level control

• Check the API reference