Hands-on: Build an End-to-End Quantum ML Model with PennyLane and Curated Marketplace Data

Hands-on: Build an End-to-End Quantum ML Model with PennyLane and Curated Marketplace Data

Hook: If you’re a developer or IT pro trying to prototype quantum machine learning but keep hitting walls — limited hardware access, fragmented tooling, and uncertain dataset licensing — this step-by-step guide shows how to build, train, and provenance a hybrid quantum-classical model using PennyLane and a curated dataset sourced from a marketplace-style repository. You’ll leave with a reproducible Jupyter notebook pattern you can adapt for research or evaluation.

Why this matters in 2026

In late 2025 through early 2026 the industry doubled down on dataset marketplaces and provenance tooling. Large infrastructure providers moved to support paid, licensed datasets and stronger provenance records (for example, recent acquisitions and consolidation of AI data marketplaces made headlines in late 2025). That trend matters to quantum ML because reproducibility and legal clarity are now prerequisites for commercial evaluation and benchmarking. Hybrid quantum-classical pipelines are increasingly integrated into ML stacks, and developers expect low-friction ways to import curated data, verify licensing, and run experiments on simulators or hardware.

What you’ll build (inverted pyramid)

By the end of this guide you will have:

• Downloaded a curated dataset from a marketplace-style API and verified its provenance and license.
• Preprocessed features and encoded them for quantum circuits.
• Implemented a hybrid PennyLane model (classical PyTorch encoder + quantum circuit) and trained it on a simulator.
• Captured reproducibility metadata (dataset hash, license, commit, device metadata) and exported it alongside model artifacts.
• Learned how to adapt the notebook to run on cloud QPUs and include job provenance information.

Prerequisites

• Python 3.9+ environment (venv or conda)
• PennyLane (pip install pennylane), PennyLane plugins (e.g., pennylane-lightning), PyTorch, pandas, scikit-learn
• Access to a marketplace API token (or use the example dataset endpoint below)
• Basic familiarity with PyTorch and quantum circuits

Install the essentials

pip install pennylane pennylane-lightning torch pandas scikit-learn requests

Step 1 — Acquire and verify curated marketplace data

A core pain point: many demos use local toy data without clear licenses. Here we show a minimal, realistic flow for fetching a dataset from a marketplace-style API and verifying provenance and license before training.

Marketplace dataset contract

Marketplace APIs increasingly return a metadata JSON with fields such as dataset_id, name, license, sha256 (checksum), provenance (creator, upload time, signature), and a direct download URL. Always check license before training — if the license disallows commercial use, stop or seek permission.

Example: fetch and verify

import requests
import hashlib
import json

API_TOKEN = "REPLACE_WITH_YOUR_TOKEN"
API_URL = "https://data-market.example/api/datasets/42"  # marketplace-like endpoint

# 1) fetch metadata
resp = requests.get(API_URL, headers={"Authorization": f"Bearer {API_TOKEN}"})
resp.raise_for_status()
meta = resp.json()

# 2) check license
license_field = meta.get("license", "unknown")
if license_field.lower() in ["no-commercial-use", "restricted"]:
    raise SystemExit("Dataset license prohibits commercial or research use. Aborting.")

# 3) download file and verify checksum
download_url = meta["download_url"]
r = requests.get(download_url)
with open("dataset.csv", "wb") as f:
    f.write(r.content)

sha256 = hashlib.sha256(r.content).hexdigest()
if sha256 != meta.get("sha256"):
    raise SystemExit("Checksum mismatch — possible tampering. Aborting.")

# 4) save provenance record
with open("provenance.json", "w") as pf:
    json.dump({"meta": meta, "download_sha256": sha256}, pf, indent=2)

Actionable advice: Build policy gates in your CI that fail if license checking or checksum verification does not pass. Save provenance.json alongside model artifacts.

Step 2 — Preprocess and encode for quantum circuits

Quantum circuits expect bounded inputs (angles). We’ll load the CSV, scale features into [0, pi/2], and split into train/test.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np

df = pd.read_csv("dataset.csv")
# assume the dataset is small and tabular; adapt as needed
X = df.drop(columns=["label"]).values
y = df["label"].values

scaler = MinMaxScaler(feature_range=(0, np.pi/2))
X_scaled = scaler.fit_transform(X)

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Save scaler parameters into provenance for reproducibility
import json
with open("preprocessing.json", "w") as f:
    json.dump({"scaler_min": scaler.data_min_.tolist(), "scaler_max": scaler.data_max_.tolist()}, f)

Tip: Record preprocessing artifacts (scaler params, PCA transforms) in the provenance bundle to guarantee deterministic transforms on any environment.

Step 3 — Define a hybrid PennyLane model

We'll implement a two-part model: a classical encoder (PyTorch) that maps features to a small latent vector, and a parametric quantum circuit (PQC) built with PennyLane that consumes that latent vector and returns expectation values. PennyLane's interface for PyTorch makes backprop seamless.

Design choices

• Number of qubits: choose based on feature dims; for prototyping use 4 qubits.
• Encoding: amplitude vs angle. We use angle encoding (single-qubit rotations) — easier to scale for hybrid workflows.
• Simulator vs hardware: start with a high-performance simulator (lightning) for fast iteration; annotate the device plugin in provenance if you run on a real QPU.

import pennylane as qml
from pennylane import numpy as pnp
import torch
import torch.nn as nn

n_qubits = 4
dev = qml.device("lightning.qubit", wires=n_qubits)  # fast simulator

# Quantum circuit
@qml.qnode(dev, interface="torch")
def circuit(inputs, weights):
    # inputs: tensor of length n_qubits (angles)
    for i in range(n_qubits):
        qml.RY(inputs[i], wires=i)
    # variational layers
    k = weights.shape[0]
    for i in range(k):
        for q in range(n_qubits):
            qml.RY(weights[i, q], wires=q)
        # entangle
        for q in range(n_qubits - 1):
            qml.CNOT(wires=[q, q + 1])
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

# Hybrid model: classical encoder + quantum layer
class HybridModel(nn.Module):
    def __init__(self, n_features, n_qubits, n_layers=2):
        super().__init__()
        self.encoder = nn.Sequential(
            nn.Linear(n_features, 8),
            nn.ReLU(),
            nn.Linear(8, n_qubits)
        )
        # quantum weights: (n_layers, n_qubits)
        weight_shape = (n_layers, n_qubits)
        self.q_params = nn.Parameter(0.01 * torch.randn(*weight_shape))
        self.fc = nn.Linear(n_qubits, 1)

    def forward(self, x):
        x_enc = self.encoder(x)
        # ensure angles are in proper dtype
        q_out = torch.stack([circuit(x_enc[i], self.q_params) for i in range(x_enc.shape[0])])
        out = self.fc(q_out)
        return torch.sigmoid(out).squeeze()

Practical note: PennyLane's batch QNode support improves performance. For larger batches you can vectorize QNodes. For small experiments the loop is acceptable; profile and switch to qml.batch if needed.

Step 4 — Train, evaluate, and capture metadata

Now we train on the simulator and store provenance data: PennyLane version, device plugin, dataset id, dataset checksum, license, preprocessing params, random seed, and training hyperparameters.

# Convert data to torch tensors
X_train_t = torch.tensor(X_train, dtype=torch.float32)
X_test_t = torch.tensor(X_test, dtype=torch.float32)
y_train_t = torch.tensor(y_train, dtype=torch.float32)
y_test_t = torch.tensor(y_test, dtype=torch.float32)

model = HybridModel(n_features=X_train.shape[1], n_qubits=n_qubits, n_layers=2)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
loss_fn = nn.BCELoss()

# training loop
n_epochs = 30
for epoch in range(n_epochs):
    model.train()
    optimizer.zero_grad()
    y_pred = model(X_train_t)
    loss = loss_fn(y_pred, y_train_t)
    loss.backward()
    optimizer.step()

    if epoch % 5 == 0:
        model.eval()
        with torch.no_grad():
            test_preds = model(X_test_t)
            test_loss = loss_fn(test_preds, y_test_t)
        print(f"Epoch {epoch} - train_loss: {loss.item():.4f} test_loss: {test_loss.item():.4f}")

# Evaluate accuracy
with torch.no_grad():
    preds = (model(X_test_t) > 0.5).numpy()
from sklearn.metrics import accuracy_score
print("Test accuracy:", accuracy_score(y_test, preds))

# Save model state and provenance
torch.save(model.state_dict(), "hybrid_model.pt")

import pennylane as qml
import os
provenance_bundle = {
    "dataset_meta": meta,
    "dataset_sha256": sha256,
    "preprocessing": json.load(open("preprocessing.json")),
    "pennylane_version": qml.__version__,
    "device": dev.short_name if hasattr(dev, "short_name") else str(dev),
    "torch_version": torch.__version__,
    "training_hyperparams": {"n_epochs": n_epochs, "optimizer": "Adam", "lr": 0.01},
    "git_commit": os.environ.get("GIT_COMMIT", "unknown"),
    "random_seed": 42
}
with open("training_provenance.json", "w") as f:
    json.dump(provenance_bundle, f, indent=2)

Actionable advice: Automate recording of versioned dependencies (pennylane, plugin, torch), environment variables, and job IDs. If you run on managed QPUs, capture the job ID and provider response and include it in training_provenance.json.

Step 5 — Running on hardware: portability and cost considerations

When you want to move from simulator to hardware, swap the device to a provider plugin (for example, quantinuum.hqs, ibm.qasm, or other vendor plugins supported by PennyLane). In 2026, providers commonly require job metadata and will return a job id; always include that id in provenance.

# Example swap (pseudocode) — adapt to provider's plugin and auth
# dev = qml.device("quantinuum.hqs", wires=n_qubits, api_key=YOUR_KEY)
# When submitting, capture the job id returned by the provider and store it in provenance

Cost & quota: Expect queue times, per-job costs, and limited shot budgets. Keep training on simulators and use hardware for final validation; collect job metadata to reproduce results later.

Provenance and licensing: checklist for responsible evaluations

Adopt a reproducibility and licensing checklist as part of every experiment:

1. Verify dataset license allows your intended use and record the license string and link.
2. Verify checksum of the downloaded dataset and keep the checksum in provenance.json.
3. Record dataset publisher, marketplace dataset_id, and any payment/invoice IDs if the dataset was purchased.
4. Record environment: PennyLane and plugin versions, PyTorch version, OS, and Python version.
5. Record hardware device or simulator name and job ids for QPU runs.
6. Record preprocessing steps, seed values, and model hyperparameters.

"Data provenance and licensing are not optional — they are part of the experiment's metadata. Treat them as first-class artifacts."

2026 trends and what they mean for your quantum ML workflows

Key trends through early 2026 that affect how you’ll execute and share quantum ML experiments:

• Marketplace maturity: Data marketplaces, consolidation, and acquisitions in late 2025 increased dataset discoverability and added stronger metadata and licensing fields (e.g., checksum, provenance signatures). This makes compliant evaluation easier if you integrate license checks into your ingestion pipeline. See recent coverage of marketplace regulation trends here.
• Provenance-first tooling: Tools for tamper-evident provenance (content-addressed storage, cryptographic signatures) are becoming standard for research-grade datasets. Start incorporating checksum and signed metadata now; practical guides on ethical pipelines are helpful here.
• Hybrid ML normalization: Hybrid quantum-classical architectures have become common prototyping patterns, integrated with classical ML frameworks such as PyTorch and JAX via PennyLane.
• Reproducibility expectations: Reviewers, auditors, and procurement teams increasingly require explicit provenance and license documentation for models used in production or paid evaluations.

Advanced strategies and further experiments

Once you have the baseline pipeline, try these advanced strategies:

• Use qml.transforms for circuit compression and expressivity analysis to compare PQC depth vs accuracy trade-offs.
• Benchmark your hybrid model across simulators and a single QPU seed; record shot noise vs accuracy curves in provenance.
• Automate dataset pre-checks in CI: implement a pre-merge job that validates license fields and checksum integrity for any dataset linked in a repository.
• Package the experiment as a shareable notebook + provenance bundle (dataset hash, preprocessing.json, training_provenance.json) so collaborators can replicate runs.

Notebook tips: reproducible, shareable, and review-friendly

• Keep cells idempotent: add a single cell to fetch and verify dataset and another to preprocess; provide flags to skip download if dataset exists.
• Embed provenance.json content at the top of the notebook as a read-only summary for reviewers.
• Use %pip install in notebook to ensure dependency parity when sharing with colleagues or CI runners.
• Include a small sample dataset (if license allows) or a synthetic generator function for quick dry-runs without network access.

Common pitfalls and how to avoid them

• Ignoring license restrictions: always programmatically enforce license checks in your ingestion pipeline.
• Not saving preprocessing artifacts: you’ll get different results without the exact scaler/PCA parameters.
• Overfitting to simulator noise-free behavior: hardware has noise; validate on hardware with small shot budgets and add noise-aware training later.
• Missing job provenance: losing job ids or provider logs prevents auditability when you run on managed QPUs.

Conclusion & actionable takeaways

• Start with a clear procurement and ingestion policy: always programmatically verify dataset license and checksum before training.
• Use PennyLane's PyTorch integration to implement hybrid models quickly and capture training provenance within the same pipeline.
• Save preprocessing artifacts and environment versions as part of your model bundle for reproducibility and auditability.
• When moving to hardware, capture provider job ids and costs, and include them in provenance records so experiments remain reproducible.

In 2026, the combination of mature data marketplaces and improved provenance tooling means your quantum ML pipelines can be both rigorous and repeatable. Treat data licensing and provenance as core engineering tasks — not afterthoughts.

Call to action

Ready to try the full notebook? Download the complete, runnable Jupyter notebook and provenance templates from our repo, swap in your marketplace API token, and run the tutorial end-to-end. If you want a hands-on walkthrough tailored to your team’s dataset licensing needs or QPU access, request a demo and we'll help you port this pipeline to cloud hardware with automated provenance and compliance checks.

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