Building an Edge-to-QPU Pipeline: Raspberry Pi 5 Meets Quantum Cloud

Building an Edge-to-QPU Pipeline: Raspberry Pi 5 Meets Quantum Cloud

Hook: If you’re a developer or IT engineer wrestling with fragmented SDKs, costly cloud cycles, and the steep learning curve of quantum experiments, this lab delivers a pragmatic path: collect sensor data on a Raspberry Pi 5, preprocess with the AI HAT+, and push compact feature vectors to a quantum cloud (simulator or QPU) for hybrid experimentation.

In 2026 the best practice for exploratory quantum projects is “smaller, nimbler, smarter” — short cycles, low-cost edge capture, and minimal quantum payloads that make the most of limited QPU time. This step-by-step lab demonstrates that workflow end-to-end with code, engineering trade-offs, and reproducible patterns you can adapt for anomaly detection, predictive maintenance, or prototype quantum machine learning (QML).

What you’ll build and why it matters (TL;DR)

• Edge stack: Raspberry Pi 5 + AI HAT+ + IMU/vibration sensor
• On-device preprocessing: denoising, windowing, feature extraction, ONNX inferencing on the AI HAT+
• Transport: compact JSON feature vectors over HTTPS or MQTT to a cloud gateway
• Quantum experiment: run a small variational circuit on a simulator or a cloud QPU (Qiskit runtime / PennyLane / Braket) that consumes the feature vector as rotation angles

Context: Why Edge-to-QPU pipelines are practical in 2026

Late 2025 and early 2026 brought two important trends that make this lab relevant:

• Edge AI hardware maturity: The AI HAT+ and similar NPUs let you run ONNX models on-device, dramatically reducing network usage and producing compact, information-rich feature vectors instead of raw sensor streams.
• Quantum cloud interoperability: Emerging standards (OpenQASM 3 adoption, QIR compatibility efforts, and broader SDK integrations between Qiskit, PennyLane, and cloud vendors) make it easier to move small quantum workloads between simulators and QPUs.

Together these shifts mean you can run many “classical” preprocessing steps on cheap hardware, and only send the distilled, low-dimensional data to the quantum layer — a practical hybrid approach that reduces cost and complexity while enabling real QPU experiments.

Prerequisites & hardware

• Raspberry Pi 5 (latest Raspberry Pi OS 2026 image)
• AI HAT+ (AI accelerator module compatible with Pi 5)
• Sensor: MPU-9250 or similar IMU, or an accelerometer for vibration capture
• Power, microSD card (32GB+), and network connectivity (Ethernet/Wi‑Fi)
• Python 3.11+, pip, and basic Linux command-line skills
• Cloud quantum access: IBM Quantum / Amazon Braket / Rigetti / IonQ account or local simulator like Qiskit Aer or PennyLane

Design choices: keep the quantum payload small

Design rule: keep the dimensionality of feature vectors under 8 where possible. QPUs are expensive and noisy; fewer qubits, shallow circuits, and hardware-efficient encoding maximize signal. The lab uses 4 features mapped to 4 rotation angles on a 4-qubit variational circuit — enough for toy classification or anomaly scoring while remaining QPU-friendly.

Step 1 — Hardware & OS setup (Pi 5 + AI HAT+)

1. Flash Raspberry Pi OS (Bullseye/2026 preferable) to the microSD and boot the Pi 5.
2. Attach the AI HAT+ to the Pi’s headers. Install vendor drivers (check AI HAT+ documentation for the latest 2026 driver package). Typical install sequence (on Pi):

   sudo apt update
   sudo apt install -y python3-pip i2c-tools git
   # vendor-specific install (example)
   git clone https://github.com/vendor/ai-hat-plus && cd ai-hat-plus
   sudo ./install.sh

3. Enable I2C/SPI via raspi-config if sensor uses those buses:

   sudo raspi-config  --> Interface Options --> I2C / SPI

4. Wire the IMU or accelerometer (SDA/SCL power/GND) and confirm with i2cdetect.

Step 2 — Sensor capture code

The goal: capture short windows (e.g., 1 second at 200 Hz), then hand them to the AI HAT+ for preprocessing and feature extraction.

import time
import numpy as np
import smbus

# Example pseudo-code for reading an I2C accelerometer
I2C_BUS = 1
bus = smbus.SMBus(I2C_BUS)
SENSOR_ADDR = 0x68

def read_raw():
    # implement sensor-specific reading; returns [ax, ay, az]
    return np.random.randn(3)  # placeholder for demo

def capture_window(duration_s=1.0, rate_hz=200):
    samples = []
    interval = 1.0 / rate_hz
    end = time.time() + duration_s
    while time.time() < end:
        samples.append(read_raw())
        time.sleep(interval)
    return np.array(samples)

Use the AI HAT+ to run small denoising or feature extraction ONNX models. The HAT+ typically exposes an API or CLI to run ONNX models accelerated on its NPU.

Step 3 — On-device preprocessing with AI HAT+

Preprocessing responsibilities on-device:

• Denoise / bandpass filter
• Windowing and FFT or time-domain features (RMS, kurtosis, spectral centroid)
• Dimensionality reduction (PCA, quantized autoencoder) or shallow ML inference
• Produce a small feature vector (4 scalars in our example)

Example: compute RMS + dominant frequency + skew + kurtosis — four features. Offload a small ONNX model for feature normalization on AI HAT+ to ensure consistent scaling across devices.

from scipy import signal, stats

def extract_features(window):
    # window shape: (N, 3) for 3-axis accel
    mag = np.linalg.norm(window, axis=1)
    rms = np.sqrt(np.mean(mag**2))
    f, Pxx = signal.welch(mag, fs=200)
    dom_freq = f[np.argmax(Pxx)]
    skew = stats.skew(mag)
    kurt = stats.kurtosis(mag)
    return np.array([rms, dom_freq, skew, kurt])

# Call vendor API to run normalized ONNX on AI HAT+ if available
# ai_hat.run_onnx('normalizer.onnx', features)

Practical tip:

Quantize the four floats to 16-bit or 8-bit fixed-point before transport to save bandwidth. Many NPUs on AI HAT+ support int8 quantized models; use that flow to maintain consistency between preprocessing and model expectations.

Step 4 — Securely transport feature vectors to the quantum gateway

Two transport patterns are common:

• Push via HTTPS: Pi posts JSON to a cloud gateway that handles authentication and queues quantum jobs.
• MQTT: Use lightweight brokers (AWS IoT Core, Mosquitto) to publish features to a backend service which then triggers quantum workflows.

import requests

GATEWAY_URL = 'https://quantum-gateway.example.com/ingest'
API_KEY = 'REPLACE_WITH_YOUR_KEY'

def send_features(features):
    payload = {'device_id': 'pi5-01', 'features': features.tolist(), 'ts': time.time()}
    headers = {'Authorization': f'Bearer {API_KEY}'}
    r = requests.post(GATEWAY_URL, json=payload, headers=headers, timeout=10)
    r.raise_for_status()
    return r.json()

Design the gateway to batch multiple feature vectors into a single quantum job when possible; batch size lets you amortize QPU runtime.

Step 5 — Mapping features to a quantum circuit

We’ll build a small variational quantum circuit where each feature maps to a rotation angle on a corresponding qubit: angle = scale(feature). This pattern is fast to implement and common in QML prototypes, and plays well with noisy hardware.

from qiskit import QuantumCircuit

def features_to_circuit(features):
    # features: array length 4
    qc = QuantumCircuit(4)
    # simple encoding: Ry rotations
    for i, f in enumerate(features):
        theta = float(f) * 0.1  # scaling factor; tune per dataset
        qc.ry(theta, i)
    # add a small entangling layer and parameterized rotations for variational part
    qc.cx(0,1); qc.cx(2,3)
    qc.ry(0.5,0); qc.ry(0.8,1); qc.ry(0.3,2); qc.ry(0.6,3)
    qc.measure_all()
    return qc

Note: In 2026, hardware-efficient ansatz and one- or two-layer parameterized circuits remain a pragmatic choice for near-term QPUs. Use lightweight error mitigation techniques (readout calibration) in your gateway to improve results.

Step 6 — Running on simulator vs QPU

Two modes are useful during development:

1. Local simulation with Qiskit Aer or PennyLane — fast and free for iteration.
2. Cloud QPU for real noise experiments — use short jobs and small circuits to keep cost and queue time low.

Example: Run locally with Qiskit Aer

from qiskit import Aer, transpile, assemble

backend = Aer.get_backend('aer_simulator')
qc = features_to_circuit(np.array([0.5, 20.0, 0.1, -0.2]))
transpiled = transpile(qc, backend)
qobj = assemble(transpiled, shots=1024)
result = backend.run(qobj).result()
counts = result.get_counts()
print(counts)

Example: Submit to a quantum cloud (Qiskit runtime-style)

Use provider SDKs and small jobs. This example uses a generic Qiskit Runtime pattern — replace with your provider's exact API and credentials.

from qiskit_ibm_runtime import QiskitRuntimeService, Session, Options

service = QiskitRuntimeService(channel='ibm_quantum', token='YOUR_IBM_TOKEN')
backend_name = 'ibm_perth_qpu'  # replace with available small-device

with Session(service=service, backend=backend_name) as session:
    program_inputs = {'circuits': [qc]}
    options = Options(shots=1024)
    job = session.run(program='circuit-runner', inputs=program_inputs, options=options)
    out = job.result()
    print(out)

Practical note: QPU gateways in 2026 increasingly support low-level job templates and batch submissions. Keep each job under a few hundred microseconds of circuit depth and a small shot count for exploratory runs.

Example use case: Vibration anomaly scoring

Workflow:

1. Pi captures vibration window and extracts 4 features.
2. Gateway collects features from several devices and sends a batch as inputs to a parametrized quantum circuit that outputs an anomaly score (simple classifier probability).
3. Use classical postprocessing (moving average) to trigger alerts.

Why use the quantum layer? In early-stage research you might explore whether a small quantum model can provide different inductive biases or complement classical classifiers. Importantly, the architecture focuses your quantum experiments on the hardest part — learning from compressed, information-rich vectors rather than ingesting raw data on QPUs.

Operational considerations & best practices

• Security: Use mutual TLS or token-based auth to protect feature payloads. Treat feature vectors as sensitive if they correlate to equipment behaviors.
• Latency: Edge preprocessing minimizes bytes sent; still expect round-trip times of seconds to minutes depending on cloud queue. Design experiments for asynchronous jobs.
• Error mitigation: Calibrate readout with short calibration circuits; use zero-noise extrapolation for noisy QPUs where possible.
• Cost control: Batch jobs and keep circuits small. Use simulators most of the time; reserve QPU runs for hypotheses you want to validate with real noise.
• Interoperability: Favor standard representations (OpenQASM 3 or serialized circuits) at the gateway to support multiple clouds. In 2026, many providers accept OpenQASM or QIR payloads.

Troubleshooting & debugging tips

• If the AI HAT+ model fails to load, check compatibility between ONNX ops and the HAT+ runtime — quantize models and test them on the HAT+ emulation tools before deployment.
• Validate feature consistency across devices with a small unit-test suite that runs periodic health checks.
• If QPU results are inconsistent, run calibration and compare simulator vs QPU with the same noise model (use provider noise models where available).

“Smaller, nimbler, smarter” projects win: focus on a single hypothesis, keep the quantum payload tiny, and iterate quickly.

Advanced strategies for 2026 and beyond

• Hybrid training: Use the cloud gateway to orchestrate hybrid classical-quantum training loops where classical optimizers run in the cloud and parameterized circuits run on QPUs.
• Federated feature aggregation: Aggregate compressed feature vectors from many Pi devices to create richer training sets without moving raw data.
• Model distillation: Distill quantum-enhanced insights back into small classical models that can run entirely at the edge for inference.
• Benchmarking pipelines: Keep reproducible pipelines with CI that runs local simulations and periodically runs cloud QPU jobs to track drift in performance over time.

Minimal reproducible checklist

1. Pi 5 + AI HAT+ functional, sensor connected.
2. ONNX normalizer and quantized feature extraction model tested on-device.
3. Secure gateway that accepts feature vectors and can submit circuit jobs to a simulator/QPU.
4. Small variational circuit that maps features to rotations (4 features → 4 qubits).
5. Logging, calibration, and simple dashboard to inspect results and compare simulator vs QPU outputs.

Actionable takeaways

• Start small: aim for 4 features and a 4-qubit circuit to reduce cost and risk.
• Leverage the AI HAT+ to minimize data transfer and standardize feature scaling across devices.
• Use local simulators for iteration and run periodic QPU experiments to validate hypotheses under real noise.
• Design the gateway to be vendor-agnostic — accept OpenQASM or serialized circuits and support multiple cloud backends.

Closing: Next steps and resources

By combining Raspberry Pi 5’s low-cost edge capture, the AI HAT+ acceleration, and modern quantum cloud runtimes, you can build a practical hybrid pipeline that lets you explore quantum-enhanced workflows without expensive raw-data transfers. In 2026, the sweet spot is rapid, focused experiments — small classical preproc + compact quantum payloads — rather than attempting to move entire ML workloads into QPUs.

Ready to try it? Clone a starter repo, build the ONNX normalizer, and schedule a single QPU validation run. Share your experiment outputs with the qbit365 community — benchmark results and failure modes are the most valuable artifacts for practitioners.

Call to action

Try the lab now: spin up your Pi, implement the 4-feature extractor, and submit a batch of 10 circuits to a simulator. When you have results, join our qbit365 forum or GitHub Discussions with your logs and we’ll help iterate on circuit design, error mitigation, and gateway scaling.

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