quprep 0.8.0

Quantum data preparation — the missing preprocessing layer between classical datasets and quantum computing frameworks

QuPrep — Quantum Data Preparation

The missing preprocessing layer between classical datasets and quantum computing frameworks.

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QuPrep converts classical datasets into quantum-circuit-ready format. It is not a quantum computing framework, simulator, or training tool — it is the preprocessing step that feeds into Qiskit, PennyLane, Cirq, TKET, and any other quantum workflow.

CSV / DataFrame / NumPy / images / text / graphs  →  QuPrep  →  circuit-ready output

What QuPrep does

• Ingest tabular data, time series, images, text, and graphs — all in the same pipeline API
• Clean, normalize, and reduce dimensionality to fit your hardware qubit budget
• Encode data into circuits using 13 encoding methods (Angle, Amplitude, IQP, ZZFeatureMap, GraphState, and more)
• Recommend, compare, and auto-select the best encoding for your dataset and task
• Export circuits to 8 frameworks: OpenQASM 3.0, Qiskit, PennyLane, Cirq, TKET, Braket, Q#, IQM
• Formulate combinatorial optimization problems as QUBO / Ising models; export as QAOA circuit templates for your quantum framework

QuPrep does not train models, simulate circuits, run on quantum hardware, or optimize variational parameters.

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Installation

pip install quprep

With optional extras:

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

# Data modalities
pip install quprep[image]      # image ingestion (Pillow)
pip install quprep[text]       # text embeddings (sentence-transformers, ~2 GB)
pip install quprep[modalities] # image + text at once

# Other
pip install quprep[umap]       # UMAP dimensionality reduction
pip install quprep[viz]        # matplotlib circuit diagrams
pip install quprep[all]        # everything

Requirements: Python ≥ 3.10. Core dependencies: numpy, scipy, pandas, scikit-learn.

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Quickstart

One-liner

import quprep as qd

result = qd.prepare("data.csv", encoding="angle", framework="qasm")
print(result.circuit)

Full pipeline

import quprep as qd

pipeline = qd.Pipeline(
    cleaner=qd.Imputer(),
    reducer=qd.PCAReducer(n_components=8),
    encoder=qd.IQPEncoder(reps=2),
    exporter=qd.PennyLaneExporter(),   # pip install quprep[pennylane]
)
result = pipeline.fit_transform("data.csv")
qnode = result.circuit   # callable qml.QNode

Data modalities — time series, images, text, graphs

import quprep as qd

# Time series — sliding window then encode
from quprep.ingest.time_series_ingester import TimeSeriesIngester
from quprep.clean.window_transformer import WindowTransformer

result = qd.Pipeline(
    preprocessor=WindowTransformer(window_size=5, step=1),
    encoder=qd.AngleEncoder(),
).fit_transform(TimeSeriesIngester(time_column="date").load("sensor.csv"))

# Images — pip install quprep[image]
from quprep.ingest.image_ingester import ImageIngester
result = qd.prepare("images/", encoding="angle", ingester=ImageIngester(size=(8, 8), grayscale=True))

# Text — TF-IDF (no deps) or sentence-transformers (pip install quprep[text])
from quprep.ingest.text_ingester import TextIngester
texts = ["quantum computing is powerful", "machine learning meets QML", ...]
result = qd.prepare(texts, encoding="angle", ingester=TextIngester(method="tfidf", max_features=16))

# Graphs — lossless graph state encoding
from quprep.ingest.graph_ingester import GraphIngester
from quprep.encode.graph_state import GraphStateEncoder
import numpy as np
graph_list = [np.array([[0,1,1],[1,0,0],[1,0,0]], dtype=float), ...]  # adjacency matrices
result = qd.Pipeline(encoder=GraphStateEncoder()).fit_transform(
    GraphIngester(features="adjacency").load(graph_list)
)

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More features

Feature	Docs
Encoding recommendation — ranked by dataset profile and task	guide
Qubit budget suggestion — NISQ-safe ceiling with reasoning	API
Side-by-side encoder comparison — depth, gates, NISQ safety	API
Data drift detection — warn when new data leaves training distribution	API
Pipeline save / load — serialize fitted pipelines, no re-fitting	API
Schema validation & cost estimation — gate count before encoding	guide
QUBO / Ising formulation — Max-Cut, TSP, Knapsack, QAOA circuits, D-Wave export	guide
Plugin system — register custom encoders and exporters	guide
Circuit visualization — ASCII (no deps) or matplotlib	API
Batch QASM export — save all samples to disk as individual files	API

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Supported encodings

Encoding	Qubits	Depth	NISQ-safe	Best for
Angle (Ry/Rx/Rz)	n = d	O(1)	✅ Excellent	Most QML tasks
Amplitude	⌈log₂ d⌉	O(2ⁿ)	❌ Poor	Qubit-limited scenarios
Basis	n = d	O(1)	✅ Excellent	Binary features / QAOA
Entangled Angle	n = d	O(d · layers)	✅ Good	Feature correlations
IQP	n = d	O(d² · reps)	⚠️ Medium	Kernel methods
Re-uploading	n = d	O(d · layers)	✅ Good	High-expressivity QNNs
Hamiltonian	n = d	O(d · steps)	⚠️ Medium	Physics simulation / VQE
ZZ Feature Map	n = d	O(d² · reps)	⚠️ Medium	Quantum kernel methods
Pauli Feature Map	n = d	O(d² · reps)	⚠️ Medium	Configurable kernel methods
Random Fourier	n_components	O(1)	✅ Excellent	RBF kernel approximation
Tensor Product	⌈d/2⌉	O(1)	✅ Excellent	Qubit-efficient encoding
QAOA Problem	n = d	O(p)	✅ Good	QAOA warm-start, problem-inspired maps
Graph State	n = nodes	O(edges)	✅ Good	Graph-structured data (lossless)

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Supported export frameworks

Framework	Install	Output
OpenQASM 3.0	(included)	str
Qiskit	pip install quprep[qiskit]	QuantumCircuit
PennyLane	pip install quprep[pennylane]	qml.QNode
Cirq	pip install quprep[cirq]	cirq.Circuit
TKET	pip install quprep[tket]	pytket.Circuit
Amazon Braket	pip install quprep[braket]	braket.Circuit
Q#	pip install quprep[qsharp]	Q# operation string
IQM	pip install quprep[iqm]	IQM circuit JSON

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Documentation

Full documentation at docs.quprep.org

• Installation
• Quickstart guide
• Encoding guide
• API reference

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Examples

#	Topic	Launch
01	Quickstart — prepare() one-liner
02	Full pipeline — clean → encode → export → save/load
03	All encoders compared
04	Framework export — QASM, Qiskit, PennyLane, Cirq, TKET, Braket, Q#, IQM
05	Encoding recommendation
06	Circuit visualization — ASCII + matplotlib
07	QUBO / Ising — Max-Cut, Knapsack, solvers, D-Wave export, QAOA
08	Validation, schema & cost
09	Data drift detection
10	Qubit suggestion — suggest_qubits, task hints, NISQ ceiling
11	Plugin system — register custom encoders and exporters
12	Data modalities — time series, image, text, graph
13	Interoperability — OpenML/HuggingFace/Kaggle connectors, inspect, benchmark, fingerprinting

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Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request.

• Open an issue for bugs or feature requests
• Start a discussion for questions or ideas

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License

Apache 2.0 — see LICENSE.

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Citation

If you use QuPrep in your research, please cite:

@software{quprep2026,
  author    = {Perera, Hasarindu},
  title     = {QuPrep: Quantum Data Preparation},
  year      = {2026},
  publisher = {Zenodo},
  version   = {0.8.0},
  doi       = {10.5281/zenodo.19286258},
  url       = {https://doi.org/10.5281/zenodo.19286258},
  license   = {Apache-2.0},
}