Pi 5 + AI HAT+: Building a Low-Cost Quantum Control and Telemetry Node

Hook: Stop shipping terabytes from your lab — do smart preprocessing at the edge

Experimental quantum labs and early-stage QPU integrations face a persistent bottleneck: massive, bursty measurement streams and fragile peripheral control that make reproducible experiments expensive and slow. What if you could place a low-cost, developer-friendly node next to the cryostat that classifies, compresses, tags, and controls in real time — and only forward the distilled information to the cloud or a central server? In 2026 this is practical: the Raspberry Pi 5 paired with the new AI HAT+ turns into an affordable, programmable quantum control and telemetry node capable of local inferencing and hardware control for experimental setups.

Why this matters in 2026

Over the past two years (late 2024–early 2026) the quantum community has moved from “connect to every QPU” to “orchestrate hybrid classical/quantum experiments with robust local telemetry.” Key trends driving this shift:

• Growing edge-AI hardware availability for Raspberry Pi-class devices (AI HAT+ and competitors) makes on-device inferencing affordable and power-efficient.
• Cloud QPU time remains scarce and expensive, so labs push more pre-processing and filtering to the edge to reduce queuing and cloud bandwidth costs.
• Quantum experiments are increasingly automated and continuous: real-time feedback and adaptive experiments need sub-ms control loops and reliable telemetry streams.

That convergence makes the Raspberry Pi 5 + AI HAT+ an attractive option for prototyping and running parts of the experiment stack locally.

What you can realistically do with a Pi 5 + AI HAT+

With minimal investment you can implement several valuable pieces of an experimental stack:

• Real-time readout classification — run small neural nets that map raw ADC waveforms to qubit states (|0>, |1>, excited populations) locally to reduce saved traces.
• Lossy/lossless compression — autoencoders or predictive encoders that shrink readout bursts by 10–50x before upload.
• Anomaly detection and drift alerts — lightweight models detect measurement chain failures, thermal spikes, or amplifier saturation and raise immediate alerts.
• Peripheral control — trigger DACs, switches, and pulse-sequencer start/stop via SPI / GPIO / UART with deterministic timing suitable for non-critical control paths.
• Telemetry tagging and indexing — add experiment metadata and timestamps, then send compact JSON/Protobuf records to your logging system, time-series DB, or a cloud QPU job manager.

System architecture: how it fits into your experiment

Here’s a practical architecture pattern you can implement today:

1. Signal acquisition: cryostat readout chain -> ADC / digitizer (connected over USB/SPI/ethernet)
2. Edge node: Raspberry Pi 5 + AI HAT+ acts as telemetry and preprocessing node
3. Local tasks: waveform buffering, inferencing (classification/compression), anomaly detection, trigger signals to control hardware
4. Sinks: compressed data + metadata -> central server / cloud; alerts -> Slack/ops; occasional full trace uploads for debugging

Why the Pi 5 is the sweet spot

The Raspberry Pi 5 provides significantly better CPU performance and I/O than earlier models, while the AI HAT+ adds a dedicated accelerator for on-device inference. That combination gives you:

• Headroom for buffering and low-latency processing
• Support for standard ML runtimes (TFLite, ONNX Runtime, vendor runtimes) on the HAT+
• Low entry cost and an ecosystem of libraries and community projects to accelerate development

Hardware and software components — shopping list

Minimal components to build a Pi 5 quantum telemetry node:

• Raspberry Pi 5 (base board)
• AI HAT+ (model with on-board NPU for INT8/FP16 inferencing)
• Digitizer/ADC (USB or SPI; e.g., 14–16 bit, sample rate as needed for your readout)
• Peripheral breakout (GPIO, SPI, I2C, UART) for controlling DACs, switches and TTL triggers
• SSD or fast microSD for logging and model storage
• Optional: industrial Ethernet or 10GbE USB NIC for fast offload

Software stack:

• Raspberry Pi OS (64-bit recommended)
• Device drivers for AI HAT+ and digitizer
• TFLite / ONNX Runtime / vendor NPU runtime
• Python 3.11+, asyncio, and data libraries (numpy, scipy)
• MQTT / ZeroMQ / gRPC client for telemetry transport
• Systemd service scripts for deterministic startup and process restart

Design patterns and practical tips

1) Keep the critical feedback path local

Use the Pi + HAT+ to implement low-latency, non-hard-real-time feedback (e.g., <100 µs to ms timescales). For hardware requiring sub-µs control, keep the FPGA/DAC near the AWG. The Pi can still orchestrate sequences and manage higher-level adaptive decisions.

2) Use model quantization and small architectures

Design models that are tiny and robust: convolutional filters for waveform features, shallow LSTMs for short-time dependencies, or tiny autoencoders for compression. Quantize to INT8/FP16 to leverage the AI HAT+ accelerator and increase throughput.

3) Buffering and batch inference

To maximize NPU utilization, batch readout windows into micro-batches where latency allows. For instance, classify 32 readouts at once instead of one-by-one when per-inference latency is dominated by HAT+ driver overhead.

4) Use early-exit and selective upload

Send only summaries for routine pulses (state labels, confidence, compressed latent vectors). Upload full traces only when the anomaly detector flags an event or on scheduled sample windows for calibration.

5) Time sync and metadata

Attach a monotonic timestamp (e.g., PTP or synced NTP) at the edge node and include experiment IDs, pulse numbers, and calibration version. This makes later alignment with QPU job logs or cloud traces straightforward.

Example pipeline — from ADC to compressed telemetry

Below is a compact, actionable pipeline you can prototype this week:

1. Digitizer captures bursts: 1024 samples per readout at your ADC sampling rate.
2. Pi 5 process collects bursts and performs light DSP (baseline subtraction, windowing).
3. Inferencing on the AI HAT+: a small CNN classifies readouts to state probabilities and outputs a compressed latent (autoencoder bottleneck).
4. Decision logic: if confidence < threshold or anomaly detected, mark for full upload; otherwise send compressed latent + metadata.
5. Telemetry transport: send messages via MQTT (TLS) or ZeroMQ to central logger or cloud ingestion service.

Minimal example: Python pseudo-code

Use this as a starting point. It assumes you have a TFLite model compiled for the HAT+ runtime and a digitizer accessible via USB.

import numpy as np
import asyncio
from tflite_runtime.interpreter import Interpreter

# load model compiled for HAT+
interpreter = Interpreter(model_path='readout_cnn.tflite', experimental_delegates=['libaihat_delegate.so'])
interpreter.allocate_tensors()

async def read_digitizer():
    # placeholder: read 1024-sample waveform from your ADC
    return np.random.randn(1024).astype(np.float32)

def preprocess(wave):
    # baseline subtract + normalize
    wave = wave - np.mean(wave)
    return (wave / (np.std(wave) + 1e-6)).reshape(1, 1024)

def infer(wave):
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()
    interpreter.set_tensor(input_details[0]['index'], wave)
    interpreter.invoke()
    out = interpreter.get_tensor(output_details[0]['index'])
    # out contains class prob or compressed latent
    return out

async def main_loop():
    while True:
        wave = await read_digitizer()
        x = preprocess(wave)
        out = infer(x)
        # simple decision: confidence threshold
        prob = out[0,0]
        if prob < 0.9:
            # mark for full upload
            send_full_trace(wave)
        else:
            send_summary(out)
        await asyncio.sleep(0)  # yield

asyncio.run(main_loop())

Compression strategies

Different labs will have different tolerances for data fidelity. Choose a strategy based on your goals:

• Lossless preprocessing: delta encoding + LZ4 for moderate reduction and guaranteed fidelity.
• Predictive encoding: use a small encoder that predicts next-sample residuals — effective for correlated waveforms.
• Autoencoders: learn a compact latent representation (e.g., 16–64 floats) that reconstructs readouts with acceptable error.
• Model-based summaries: output classical features (decay times, amplitude, SNR) instead of full waveform data.

Security, reliability and observability

Even cheap edge nodes must be production-minded in a lab environment:

• Secure transport: use TLS for telemetry and mutual auth where possible. TLS and mutual-auth patterns are standard by 2026.
• Process isolation: run inferencing in a container or isolated runtime; use systemd to auto-restart on failure.
• Watchdog and health checks: periodic heartbeats with per-node stats (CPU, temperature, queue lengths) — pair these with site resilience plans (battery or portable backup) where needed; see field power and resilience reports such as portable solar chargers & power resilience.
• Firmware and model management: sign and version models; deploy model updates through a secure pipeline to prevent drift.

Integration with quantum software stacks

By 2026, most major quantum SDKs (Qiskit, Cirq, PennyLane) support metadata hooks and asynchronous telemetry. Use these hooks to:

• Annotate measurement runs with experiment IDs and parameter sweeps.
• Retrieve compressed results and feed them to the classical optimizer for closed-loop calibration.
• Ship summary statistics to cloud QPU schedulers so you can co-schedule classical preprocessing and quantum jobs — ingest these into a time-series/analytics backend (see notes on analytics backends for scale).

Case study: local readout compression reduced upload costs by 20x

At a small mid-2025 lab pilot, a Pi 5 + HAT+ prototype ran an autoencoder-based compressor on readouts across 4 qubits. The lab reported typical reduction ratios of 12–25x for routine readout bursts and a 20x reduction in monthly cloud storage costs. The autoencoder was a 3-layer CNN encoder/decoder quantized to INT8 and deployed through the vendor runtime on the HAT+. Anomaly detection logic flagged <2% of bursts for full upload, enabling rapid debugging while minimizing routine telemetry volume.

Limitations and when not to use this approach

Understand the trade-offs:

• Hard real-time control: If your experiment needs sub-µs deterministic triggering, use FPGA-based control close to the DAC. The Pi node is best for higher-level control and telemetry.
• Very high throughput digitizers: If you’re streaming 10s of GB/s you’ll need more powerful edge hardware or a direct PCIe-based solution.
• Model validation: Compression or classification models must be validated against ground truth to avoid subtle biases in your experiment data.

Advanced strategies and future-proofing (2026+)

• Federated calibration: run lightweight calibration routines on many Pi nodes and aggregate model updates centrally to avoid shipping raw calibration traces.
• Adaptive models: allow on-device transfer learning to adapt inference to drift without moving data off-site; keep weights signed and versioned.
• Hybrid pipelines: use the Pi node as a staging area for batched uploads that trigger cloud-based heavy analysis only when needed.
• Composable telemetry: use schematized Protobuf payloads so downstream analyzers can understand compressed latents and reconstruct when required.

Practical rule of thumb (2026): if your experiment produces repetitive, high-volume readout bursts, plan to move classification and compression to the edge — it pays off in reduced cloud cost and faster iteration.

Getting started checklist

1. Buy a Pi 5 and AI HAT+. Set up Raspberry Pi OS (64-bit), enable SSH, and secure the device with a strong admin user.
2. Install HAT+ drivers and the vendor runtime. Test the runtime with a sample TFLite/ONNX model.
3. Wire your digitizer and validate readout capture in Python. Add timestamping and minimal metadata.
4. Prototype a tiny classifier (1–2 conv layers) or autoencoder in PyTorch/TensorFlow. Quantize to INT8 and validate accuracy vs. floating-point baseline.
5. Deploy and test the inference loop. Measure latency, throughput, and end-to-end compression ratio.
6. Integrate telemetry transport (MQTT/ZeroMQ) and set up alerting for anomalies and health checks.

Actionable takeaway

Stop treating every lab PC as a dumb forwarder. The Raspberry Pi 5 + AI HAT+ is a practical, low-cost building block to implement local inferencing, compression, and peripheral control for quantum experiments. Within a weekend you can prototype a node that reduces telemetry volumes by an order of magnitude and speeds up your calibration/feedback loops.

Call to action

Ready to try it? Start with a single Pi 5 + AI HAT+ at a testbench readout channel and implement the minimal pipeline above. Measure compression ratios and error rates before scaling horizontally. Share your results with the community and link models and config files in a shared repo so teams can reproduce and build on your work.

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