QuPrep — Quantum Data Preparation

The missing preprocessing layer between classical datasets and quantum computing.

QuPrep converts classical tabular datasets into quantum-circuit-ready format. It sits between your data and whichever quantum framework you use — Qiskit, PennyLane, Cirq, TKET, Amazon Braket, Q#, or IQM — without locking you into any one of them.

Think of it as the pandas of quantum data preparation: focused, composable, framework-agnostic.

CSV / DataFrame / NumPy  →  QuPrep  →  circuit-ready output for your framework

What it does

• 11 encoding methods — Angle, Amplitude, Basis, IQP, Entangled Angle, Data Re-uploading, Hamiltonian, ZZFeatureMap, PauliFeatureMap, Random Fourier, Tensor Product
• 8 export targets — Qiskit, PennyLane, Cirq, TKET, Amazon Braket, Q#, IQM, OpenQASM 3.0
• Intelligent recommendation — dataset-aware encoding selection with ranked alternatives
• Hardware-aware reduction — auto-reduces features to fit a backend's qubit budget
• QUBO / Ising — formulate and solve combinatorial optimization problems (Max-Cut, TSP, Knapsack, Portfolio, and more)
• Plugin registry — register custom encoders and exporters that work with the same one-liner API

Install

pip install quprep

Quick example

import quprep as qd

result = qd.prepare("data.csv", encoding="angle", framework="qiskit")
print(result.circuit)   # qiskit.QuantumCircuit
print(result.cost)      # gate count, depth, NISQ safety

Links

• 📦 PyPI: pypi.org/project/quprep
• 📖 Docs: docs.quprep.org
• 🌐 Website: quprep.org
• 💻 Source: github.com/quprep/quprep
• 🎯 Demo: huggingface.co/spaces/quprep/demo

Apache 2.0 license · Python ≥ 3.10 · Independent research project