Workshop: Hands-on Hybrid Quantum-Classical ML Using Raspberry Pi 5 and Cloud QPUs

Hook: Why you should run this workshop at your next meetup

Developers and IT teams tell us the same thing: they want practical, hands-on quantum experience that fits into a single afternoon — not months of theory. Queues for cloud QPUs, opaque SDK differences, and uncertainty about where quantum actually helps create a high barrier to entry. This workshop curriculum removes those blockers by combining Raspberry Pi 5 on-device preprocessing with small, well-scoped cloud QPU kernels for optimization. Attendees leave with a working hybrid ML pipeline, reproducible code, and a playbook to iterate locally or scale to other edge devices.

What this meetup delivers (most important first)

• Concrete deliverable: a complete hybrid pipeline — sensor/image preprocessing on Raspberry Pi 5, then a remote quantum kernel (QAOA or quantum kernel method) invoked as a classical-quantum subroutine.
• Hands-on skills: Pi setup & edge inference, credentialed cloud QPU access, a PennyLane/Qiskit example that runs on a real cloud QPU, and techniques for latency/cost minimization.
• Outcomes: benchmark data (latency, cost, solution quality) and a GitHub repo for follow-up projects and reproducible demos.

Why this matters in 2026

The quantum ecosystem matured rapidly through late 2025: cloud providers expanded pay-as-you-go QPU access and hybrid runtimes became more standardized. Meanwhile, edge hardware — notably the Raspberry Pi 5 with AI HAT+2 — made capable on-device preprocessing affordable and reliable for meetups and classroom use.

"Your Raspberry Pi 5 just got a major functionality upgrade" — ZDNET (context: AI HAT+2 enabling stronger edge AI on Pi 5)

Combine both trends and you get a practical axis for developers: do the heavy, deterministic preprocessing locally, and reserve scarce quantum time for tiny but strategically-chosen optimization kernels. That’s the hybrid pattern we teach in this workshop.

Who should attend

• Developers curious about quantum programming and hybrid toolchains
• IT admins who provision edge devices and cloud resources
• Data scientists who want to benchmark small quantum kernels vs classical heuristics

Prerequisites (what attendees should bring or know)

• Basic Python skills (functions, packages, pip/venv)
• Familiarity with Git and SSH
• Laptop with Wi-Fi and a USB-C or micro HDMI adapter
• Optional: a Raspberry Pi 5 with AI HAT+2 (we provide a shared pool if attendees don’t have one)

Materials & infrastructure checklist for organizers

• Raspberry Pi 5 units (1 per 2–3 attendees is fine for pair programming)
• AI HAT+2 or comparable edge accelerator for on-device preprocessing
• SD images pre-flashed with Pi OS, Python 3.11+, and workshop packages
• Cloud QPU accounts (IBM Quantum, AWS Braket, Azure Quantum, Rigetti or other) with API tokens and quota set up ahead of time
• Wi-Fi, power strips, HDMI for demos, projector for instructor station
• GitHub repo with starter code, slides, and issue tracker

Workshop agenda (half-day, 3.5 hours)

1. 15 minutes — Welcome, goals, and architecture overview (edge + cloud QPU)
2. 45 minutes — Raspberry Pi 5 hands-on: capture, preprocess, and run a tiny edge model
3. 15 minutes — Break / provisioning QPU credentials
4. 60 minutes — Quantum kernel demo: formulate a small optimization, implement QAOA/QKernel, run on cloud QPU or noisy simulator
5. 45 minutes — Integrate the pipeline end-to-end, measure latency/cost, troubleshooting
6. 20 minutes — Wrap-up: benchmarks, next steps, and community resources

Curriculum detail — part 1: On-device preprocessing on Raspberry Pi 5

Learning goals

• Boot and configure a Pi 5 image for the workshop
• Use the AI HAT+2 for fast inferencing and preprocessing
• Produce a compact feature vector suitable for a remote quantum kernel

Key steps (instructor notes)

1. Provision an SD image that includes OpenCV, numpy, tflite-runtime (or ONNX runtime), and the workshop Python package.
2. Capture a small sample dataset (e.g., 64x64 grayscale images or 8-channel sensor vectors) and show how to normalize, quantize, and downsample on-device.
3. Export a fixed-size binary or low-precision integer vector (8–16 features) to keep the QPU kernel small and tractable.

Code snippet: preprocessing example (Pi 5 Python)

import cv2
import numpy as np

# Simple edge preprocessing: resize, grayscale, histogram-of-edges
img = cv2.imread('frame.jpg')
img = cv2.resize(img, (64, 64))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
# Pool to 8 features: mean edge intensity in 8 vertical bands
features = np.mean(edges.reshape(8, 8, 64), axis=(1, 2))
# Normalize and quantize to 8-bit
features = ((features - features.min()) / (features.ptp() + 1e-9) * 255).astype(np.uint8)
# Save locally for upload to quantum kernel
np.save('features.npy', features)

Tip: run local inference using the AI HAT+2 accelerator where available to offload compute-intensive transforms and keep latency low.

Curriculum detail — part 2: Small optimization kernels on cloud QPUs

Why small kernels?

Cloud QPUs are best used for tight, well-scoped problems that map to short-depth circuits: small combinatorial optimizations, parameterized variational circuits for feature maps, or quantum kernels for classification. In a meetup setting we target problems with 6–12 qubits (or fewer) to keep queue times and error rates manageable.

Suggested exercise: Feature selection as a combinatorial optimization

Use the preprocessed 8–16 feature vector and pose a constrained selection problem: select k features that maximize a small surrogate utility (e.g., mutual information, or a simple predictive metric measured on a tiny validation set). Formulate this as a binary optimization and apply QAOA with penalty terms for the cardinality constraint.

High-level pipeline

1. Load features.npy on laptop/host (the Pi can push this file via scp or MQTT)
2. Construct a binary cost function C(x) over the features
3. Implement a parameterized QAOA circuit (p=1 or 2) in PennyLane or Qiskit
4. Use a classical optimizer (COBYLA, SPSA) to minimize expectation values via remote QPU calls
5. Return chosen feature indices and apply them to a small on-device or cloud classifier to evaluate

Code snippet: PennyLane-style QAOA skeleton (replace device with your provider)

import pennylane as qml
from pennylane import numpy as np

n_qubits = 8
p = 1

# Placeholder cost Hamiltonian (diagonal) for feature selection
W = np.array([0.9, 0.3, 0.6, 0.2, 0.4, 0.5, 0.7, 0.1])

dev = qml.device('braket.local.qubit', wires=n_qubits)  # Replace with provider device

@qml.qnode(dev)
def qaoa_circuit(gamma, beta):
    # Start in uniform superposition
    for i in range(n_qubits):
        qml.Hadamard(wires=i)
    # Cost layer (diagonal in Z)
    for i in range(n_qubits):
        qml.RZ(2 * gamma * W[i], wires=i)
    # Mixer layer
    for i in range(n_qubits):
        qml.RX(2 * beta, wires=i)
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

# Classical objective based on expectation values (map expec to bitstrings in practice)
def objective(params):
    gamma, beta = params
    exps = qaoa_circuit(gamma, beta)
    # Simple surrogate: negative weighted sum -> minimize
    return -np.dot(exps, W)

init = np.array([0.1, 0.1])
opt_params = qml.optimize.NesterovMomentumOptimizer(stepsize=0.2).step(objective, init)
print('Optimized params:', opt_params)

Notes for instructors: replace 'braket.local.qubit' with your provider device string and configure credentials in advance. Use a noisy simulator for rapid experimentation and submit to the real QPU for the final run.

Hybrid orchestration patterns to teach

• Warm-start locally: run classical optimizers and simulators locally to find promising initial parameters before burning QPU time.
• Batch QPU calls: aggregate multiple circuit evaluations per job to reduce cloud call overhead.
• Mixed-precision data: quantize on-device to minimize network transfer and simplify encoding to qubits.
• Asynchronous execution: submit QPU jobs and poll results while continuing other preprocessing steps on-device.

Measurement, benchmarking and expected results

Set realistic KPIs for a meetup: you should be able to measure latency (Pi preprocessing time, upload time, queue/execute time on QPU), monetary cost per QPU run, and solution quality vs a simple classical heuristic (greedy selection or simulated annealing).

Suggested metrics

• Edge preprocessing latency (ms)
• Data sent to cloud (KB)
• QPU wall-clock time (s) including queue
• Cost per job (provider billing)
• Solution quality (objective value) relative to classical baseline

Troubleshooting & best practices

• Queue times can vary: have a simulated fallback so attendees don’t stall. Reserve a small QPU quota for the workshop if possible.
• Keep circuits shallow (p ≤ 2) and qubit counts small (≤ 12) to reduce error rates and cost.
• Cache results from repeated measurements during parameter search to avoid duplicate QPU calls.
• For data privacy, only send anonymized or aggregated features off-device; do not upload raw images unless consented.
• Use zero-noise extrapolation or readout error mitigation where available to improve observed results.

Advanced extensions (for multi-session meetups or hackdays)

• Swap the QAOA kernel for a quantum kernel SVM and compare classification accuracy.
• Benchmark multiple cloud providers and present a short vendor comparison by cost, latency, and fidelity.
• Introduce hybrid workflows with edge orchestration frameworks (e.g., K3s + MQTT) to deploy the Pi as a persistent inference node.
• Integrate an automated CI workflow that runs a monthly QPU job to validate drift and model performance.

Security, privacy and governance notes

When you integrate edge devices and cloud QPUs you must be deliberate about data handling. Keep the following in your workshop checklist:

• Minimize sensitive data transfer — aggregate and anonymize on-device.
• Use short-lived API tokens and rotate credentials after the event.
• Encrypt payloads in transit (HTTPS/TLS) and at rest if cached on shared machines.
• Document provider terms of use — some QPU vendors have specific usage policies for sensitive workloads.

Common questions you’ll get from attendees

Q: What problems actually benefit from a quantum kernel in this setup?

A: Primarily small combinatorial tasks and kernelized classification where feature maps or bitwise encodings can exploit quantum interference. In 2026, you should think of cloud QPUs as accelerators for targeted subroutines — not as a replacement for classical pipelines.

Q: How real are the results when running on noisy QPUs?

A: Noise can obscure small advantages. Focus the workshop on tooling, integration patterns, and reproducible benchmarking. Use mitigation and hybrid strategies to surface signal where possible.

Q: Which SDK should we use?

A: For meetups, pick a high-level hybrid-friendly SDK like PennyLane (multi-backend) or Qiskit (IBM Quantum) depending on provider support. The curriculum includes abstracted code that can be swapped between backends with small edits.

2026 trends and future predictions (what to watch next)

• Expect further standardization of quantum-classical runtimes — more providers will expose batched hybrid primitives and autoscaling for variational workflows.
• Edge accelerators (like AI HAT+2 on Pi 5) will continue to reduce preprocessing latency and power draw, making more complex encoding schemes feasible on-device.
• Cost-per-job will fall, but the practical advantage will still come from software patterns: smart orchestration, warm starts, and selective use of QPUs.

Deliverables you should publish after the event

• GitHub repo: starter code, Pi SD image link, and instructions for provider credential setup.
• Post-event notebook: reproducible runs comparing simulator and a real QPU execution.
• Benchmark report: latency, cost, and solution quality graphs for a sample problem.
• Short how-to video showing end-to-end pipeline and troubleshooting tips.

Sample Git workflow for participants

1. Fork the workshop repo before the meetup
2. Create a feature branch per exercise (e.g., pi-preprocess, qaoa-kernel)
3. Open PRs with results and a short write-up to share with the group

Final instructor checklist

• Pre-flash SD images and test all Pi units
• Pre-create cloud QPU accounts and allocate quotas
• Run the full demo end-to-end the week before the meetup
• Prepare fallback plans with simulators in case of unexpected provider outages

Actionable takeaways (what attendees will actually do)

• Configure a Raspberry Pi 5 and run an AI HAT+2 accelerated preprocessing pipeline.
• Formulate a small combinatorial optimization problem from device features.
• Implement and run a QAOA/quantum kernel on a cloud QPU provider from their laptop.
• Collect and publish a benchmark comparing classical vs quantum approaches.

Closing: run this workshop and grow your community

This curriculum turns abstract quantum concepts into a tangible developer skill: combining edge compute and cloud QPUs to solve focused problems. In 2026 the value is less about immediate superiority of quantum and more about building fluency in hybrid workflows — the teams who master these patterns will be ready to exploit larger QPUs and emerging quantum accelerators.

Ready to run this at your next meetup? Fork our starter repo, adapt the SD image to your hardware pool, and invite developers to pair-program through the exercises. Share results back to the project so the curriculum can evolve with new provider features and hardware.

Call to action

Get the workshop kit: clone the GitHub starter repo, pre-flash the SD image, and reserve a small QPU quota. Host the session, then publish the benchmark and a short demo video. If you want us to help run the first event, join qbit365.co.uk’s community board and drop a request — we’ll connect you with mentors and a tested materials list.

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