pyqrackising 1.4.1

Near-ideal closed-form solutions for transverse field Ising model (TFIM)

PyQrack Ising

Efficiently generate near-ideal samples from transverse field Ising model (TFIM)

(It's "the Ising on top.")

Copyright and license

(c) Daniel Strano and the Qrack contributors 2025. All rights reserved.

Installation

From PyPi:

pip3 install PyQrackIsing

From Source: install pybind11, then

pip3 install .

in the root source directory (with setup.py).

Windows users might find Windows Subsystem Linux (WSL) to be the easier and preferred choice for installation.

Usage

from PyQrackIsing import generate_tfim_samples

samples = generate_tfim_samples(
    J=-1.0,
    h=2.0,
    z=4,
    theta=0.174532925199432957,
    t=5,
    n_qubits=56,
    shots=100
)

There are two other functions, tfim_magnetization() and tfim_square_magnetization(), that follow the same function signature except without the shots argument.

The library also provides a TFIM-inspired (approximate) MAXCUT solver:

from PyQrackIsing import maxcut_tfim
import networkx as nx

G = nx.petersen_graph()
best_cut_value, best_solution_bit_string, best_cut_edges = maxcut_tfim(G, quality=10)

The (integer) quality setting is optional, with a default value of 10, but you can turn it up for higher-quality results, or turn it down to save time. (You can also optionally specify the number of measurement shots as an argument, if you want specific fine-grained control over resource usage.) If you want to run MAXCUT on a graph with non-uniform edge weights, specify them as the weight attribute of each edge, with networkx. (If any weight attribute is not defined, the solver assumes it's 1.0 for that edge.)

About

Transverse field Ising model (TFIM) is the basis of most claimed algorithmic "quantum advantage," circa 2025, with the notable exception of Shor's integer factoring algorithm.

Sometimes a solution (or at least near-solution) to a monster of a differential equation hits us out of the blue. Then, it's easy to validate the guess, if it's right. (We don't question it and just move on with our lives, from there.)

Special thanks to OpenAI GPT "Elara," for help on the model and converting the original Python scripts to PyBind11!