Divi

Generate, parallelize, and execute quantum programs at scale.

Divi is a Python library by Qoro Quantum for building and running quantum programs at scale. It handles circuit generation, job parallelization, and cloud execution — with built-in support for variational algorithms, custom workflows, and more — so you can focus on the quantum problem, not the plumbing.

📦 Installation

pip install qoro-divi

That covers local and cloud execution, VQE, QAOA, and program ensembles. Heavier features live behind optional extras — pennylane (PennyLane circuits, operators, and molecules), aer (Qiskit Aer backends), qubo-decompose (partitioned QUBO solving), chem (PySCF/OpenFermion chemistry), ai, and jupyter — or pip install "qoro-divi[all]" for everything. See the installation guide for what each unlocks and what keeps working without it.

Nightly Builds

To install the latest development build (published daily from main):

pip install qoro-divi --pre

Learning Divi

For the shortest introduction, start with the five-minute tutorial. Algorithm guides, execution guides, and the API reference live at divi.readthedocs.io; hands-on examples are in the tutorials/ folder.

⚡ At-Scale Example

Split a graph into quantum-sized MaxCut problems, solve the partitions, and stitch their candidates into a global solution:

import networkx as nx

from divi.backends import MaestroSimulator
from divi.qprog import BeamSearchStrategy
from divi.qprog.optimizers import ScipyOptimizer, ScipyMethod
from divi.qprog.problems import GraphPartitioningConfig, MaxCutProblem
from divi.qprog.workflows import PartitioningProgramEnsemble

graph = nx.barbell_graph(4, 0)
problem = MaxCutProblem(
    graph,
    config=GraphPartitioningConfig(
        max_n_nodes_per_cluster=4,
        partitioning_algorithm="kernighan_lin",
    ),
)

backend = MaestroSimulator()
ensemble = PartitioningProgramEnsemble(
    problem=problem,
    n_layers=1,
    backend=backend,
    optimizer=ScipyOptimizer(method=ScipyMethod.COBYLA),
    max_iterations=10,
    seed=42,
)

ensemble.run()
cut, _ = ensemble.aggregate_results(
    strategy=BeamSearchStrategy(beam_width=3, n_partition_candidates=5)
)
print(f"Cut edges: {nx.cut_size(graph, cut)}")
print(f"Circuits executed: {ensemble.total_circuit_count}")

PartitioningProgramEnsemble handles decomposition, parallel execution, and candidate aggregation while each partition remains small enough for the chosen backend.

🌐 Cloud Execution with Qoro Service

Run the same workflow on Qoro's cloud platform by swapping only the backend:

from divi.backends import QoroService

backend = QoroService()  # reads QORO_API_KEY from .env or environment

Get started for free → Sign up at dash.qoroquantum.net and receive $100 worth of credits to run your first quantum programs on our cloud.

🤖 divi-ai: AI Coding Assistant

Ask questions about Divi directly in your terminal — no API keys, no internet required after setup.

pip install qoro-divi[ai]
divi-ai

Answers questions about Divi APIs, generates code examples, and explains concepts — powered by a local LLM that runs entirely on your machine. See the full documentation for model options and usage.

🧩 Key Features

Contributing

Contributions are welcome! See CONTRIBUTING.md for development setup, testing, and code style guidelines.

📄 License

Apache 2.0 — see LICENSE for details.