Installation¶

Requirements¶

Python ≥ 3.10
Core dependencies (installed automatically): numpy, scipy, pandas, scikit-learn

Install¶

pip install quprep

Optional dependencies¶

Install only what you need.

Quantum framework exporters¶

pip install quprep[qiskit]     # Qiskit QuantumCircuit export
pip install quprep[pennylane]  # PennyLane QNode export
pip install quprep[cirq]       # Cirq Circuit export
pip install quprep[tket]       # TKET/pytket Circuit export
pip install quprep[braket]     # Amazon Braket Circuit export
pip install quprep[qsharp]     # Q# / Azure Quantum export
pip install quprep[iqm]        # IQM native format export
pip install quprep[frameworks] # All framework exporters at once

draw_ascii() is always available with no extra dependencies.

Data modalities¶

pip install quprep[image]        # Image ingestion (Pillow)
pip install quprep[text]         # Text embeddings (sentence-transformers + PyTorch, ~2 GB)
pip install quprep[huggingface]  # HuggingFace Datasets connector (datasets library)
pip install quprep[kaggle]       # Kaggle dataset/competition connector (kaggle API)
pip install quprep[openml]       # OpenML dataset connector (openml library)
pip install quprep[modalities]   # All modality extras at once

Text embedding size

quprep[text] installs sentence-transformers, which requires PyTorch (~1–2 GB). If you only need lightweight TF-IDF embeddings (no neural models, no extra deps), use TextIngester(method="tfidf") or HuggingFaceIngester(text_method="tfidf") — both work with the base quprep install.

Dimensionality reduction¶

pip install quprep[umap]       # UMAP reducer (umap-learn + numba, ~500 MB)

Visualization¶

pip install quprep[viz]        # matplotlib circuit diagrams

Mix and match¶

pip install quprep[iqm,text]                             # IQM export + text ingestion
pip install quprep[huggingface,kaggle,openml,image,text] # all data ingestion extras
pip install quprep[frameworks,modalities,viz]            # everything except UMAP and dataset connectors
pip install quprep[all]                                  # all extras including UMAP, HuggingFace, Kaggle, and OpenML

Verify¶

python -c "import quprep; print(quprep.__version__)"

Development install¶

Install uv first:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then:

git clone https://github.com/quprep/quprep.git
cd quprep
uv sync --extra dev
uv run pytest

CLI¶

After installing, the quprep command is available:

quprep --version
quprep convert data.csv --encoding angle