This release is a pre-release and may not be stable for production use.
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.
[!IMPORTANT] Divi is under active development. Expect breaking changes between minor versions.
[!TIP] Using Claude Code, Cursor, or another LLM coding agent? Divi is indexed on Context7 — point your agent at
/qoroquantum/divito pull current, version-specific Divi docs and snippets directly into its context.
⚡ At-Scale Example
For the shortest introduction, start with the five-minute tutorial.
pip install qoro-divi
Nightly Builds
To install the latest development build (published daily from main):
pip install qoro-divi --pre
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
| Feature | Description |
|---|---|
| VQE & QAOA | Built-in variational algorithms with pluggable ansätze and optimizers |
| Circuit Pipelines | Expand → execute → reduce pattern for complex circuit workflows |
| Program Ensembles | Parallel execution of multiple quantum programs with automatic scheduling, over one round or many adaptive ones |
| Flexible Backends | MaestroSimulator for local simulation, QiskitSimulator for Qiskit-native noise models, QoroService for cloud execution |
| Execution Config | Control bond dimension, simulator type, and simulation method per job |
| Live Reporting | Real-time dashboards and convergence tracking via callbacks |
🏗️ Architecture
divi/
├── qprog/ # Quantum programs: VQE, QAOA, base classes, optimizers
├── backends/ # Execution backends: MaestroSimulator, QiskitSimulator, QoroService
├── circuits/ # MetaCircuit templates and Circuit instances
├── pipeline/ # Circuit pipeline stages (expand, execute, reduce)
├── hamiltonians # Molecular Hamiltonian generation
├── reporting/ # Live reporting and visualization callbacks
└── ai/ # Offline documentation chatbot (divi-ai)
📚 Documentation
Algorithm guides, execution guides, and API reference: divi.readthedocs.io
Hands-on examples are in the tutorials/ folder.
Contributing
Contributions are welcome! See CONTRIBUTING.md for development setup, testing, and code style guidelines.
📄 License
Apache 2.0 — see LICENSE for details.
2 maintainers
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