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Rapid Quantum PoCs: A 2-Week Playbook Using Edge Hardware and Autonomous Dev Tools

UUnknown

2026-02-19

9 min read

Build measurable quantum PoCs in two weeks with a Raspberry Pi + AI HAT+ and autonomous dev tools. Includes CI, validation, and demo scripts.

Hook: Ship a measurable quantum PoC in two weeks — without expensive lab time

Quantum projects stall because of tooling friction, hardware access limits, and unclear ROI. This playbook shows a practical, repeatable path: combine a Raspberry Pi 5 with the new AI HAT+, modern autonomous dev assistants, and cloud quantum backends to deliver a measurable quantum proof‑of‑concept (PoC) in two weeks — complete with CI, validation, and a stakeholder demo script.

Why this matters in 2026

Late 2025 and early 2026 marked two key shifts: edge devices became capable of running nontrivial ML inference (the Raspberry Pi 5 + AI HAT+ made on‑device generative and inference tasks feasible), and autonomous developer tools matured into trustworthy agents that can scaffold repositories, generate tests, and manage CI flows. Together these let teams run hybrid quantum‑classical PoCs faster and cheaper than ever.

Trend: organizations are moving from big, undefined quantum R&D to focused, measurable micro‑PoCs that prove value and integration paths.

What this playbook delivers

Two‑week sprint plan (day‑by‑day tasks)
Reference architecture: Pi + AI HAT+ orchestrator + cloud quantum backends
Autonomous assistant usage patterns for code generation and testing
CI strategy and test matrix for simulation and cloud smoke tests
Validation metrics, demo scripts, and stakeholder artifacts

Before you start: required hardware, accounts, and stack

Hardware

Raspberry Pi 5 (8GB recommended)
AI HAT+ (for on‑device ML inference and accelerated workloads)
SSD or fast microSD (64GB+), power supply, case
Optional: USB 4G/5G or Ethernet for reliable network during cloud calls

Cloud and service accounts

IBM Quantum / Amazon Braket / IonQ account (pick one primary and one secondary backend for redundancy)
GitHub (or GitLab) for repo + CI
Container registry (GitHub Container Registry, Docker Hub)
Logging/monitoring (CloudWatch, Grafana, or a simple ELK stack)

Software stack

Python 3.10+ environment on the Pi
Lightweight quantum SDKs: Qiskit (for circuit building & remote runs) or PennyLane (for hybrid workflows)
Lightweight local orchestration: FastAPI or simple Flask service to expose jobs from the Pi
Autonomous dev tool: Anthropic/Cowork or Claude Code, GitHub Copilot/Pro or a hosted agent for scaffolding and CI generation
Testing: pytest + hypothesis; lightweight mocking for quantum backends

Design pattern: Hybrid edge orchestrator + cloud quantum backends

Keep the heavy lifting in the cloud (simulation, QPU queuing). Use the Pi + AI HAT+ as the local orchestrator that:

Preprocesses classical data with on‑device models (AI HAT+)
Generates parameter sets or classical controls for quantum circuits
Calls cloud quantum APIs, collects results, and runs local validation
Runs autonomous agents that create code, tests, and smoke runs on demand

Two‑week sprint: Day‑by‑day tactical plan

Assume a small team: 1 dev lead, 1 quantum dev, 1 infra/CI owner, plus a product owner for stakeholder alignment.

Sprint prep (prior to Day 1)

Define the success metric for the PoC (error reduction, latency, throughput, cost per run or business KPI mapped to quantum step)
Choose target use case (e.g., parameter tuning, small variational algorithm, or hybrid classifier)
Reserve cloud quantum credits and create API keys

Week 1 — Build the baseline

Day 1: Repo + scaffolding

Spin up repository and README with success metrics
Use an autonomous assistant to scaffold code templates: orchestrator, quantum client, CI workflow
Create an issue tracker with sprint tasks

Day 2: Local orchestration service on Pi

Install Python, lightweight web server, and SDKs on Pi
Deploy a stub FastAPI app that accepts job requests and queues them to cloud backends

Day 3: On‑device ML for preprocessing

Use AI HAT+ to run a compact model (quantized) that produces classical parameters for circuits
Benchmark inference time — record as part of demo metrics

Day 4–5: Circuit prototype and local simulation

Implement the core quantum circuit in Qiskit or PennyLane
Run local lightweight simulations to validate correctness and metric observability

Day 6–7: Autonomous agent completes tests and CI

Use the autonomous dev assistant to generate pytest cases and a GitHub Actions workflow that includes:

Static checks (flake8, mypy)
Unit tests with mocked backends
Integration smoke test that can be toggled to hit cloud QPU or simulator

Merge into main and ensure CI runs

Week 2 — Integrate, validate, and demo

Day 8: Cloud runs and data collection

Push small jobs to the chosen quantum backend — use short queue times and low shot counts for smoke data
Collect raw results and calibrations

Day 9–10: Validation & metric computation

Compute fidelity, error bars, and the PoC success metric
Compare simulation vs real backend (delta is a key talking point for stakeholders)

Day 11: CI smoke tests & nightly runs

Enable scheduled runs in CI that either use low‑cost simulators or one reserved cloud QPU slot for nightly validation
Push test reports and trace logs to an artifacts store

Day 12: Prepare demo artifacts

Create a short slide deck with metrics, architecture diagram, and demo script
Record a 3–5 minute live demo that runs a pre-recorded cloud job and then a live infer on the Pi

Day 13–14: Stakeholder demo & retrospective

Run live demo: show Pi preprocessing, cloud job submission, result aggregation, and metric visualization
Collect feedback and identify follow‑ups and scaling pathways

Concrete code snippets and CI examples

Below are minimal examples you can adapt. The goal is clarity over completeness.

Orchestrator (Pi) — job submitter (Python)

from fastapi import FastAPI
import requests
import json

app = FastAPI()

CLOUD_QAPI = "https://quantum.example.com/submit"
API_KEY = "${QUANTUM_API_KEY}"

@app.post('/submit')
def submit(job: dict):
    # preprocess on AI HAT+ (placeholder)
    params = job.get('params', {})
    # submit to cloud quantum API
    resp = requests.post(CLOUD_QAPI, headers={"Authorization": f"Bearer {API_KEY}"}, json={"circuit": job['circuit'], "params": params})
    return resp.json()

Small Qiskit circuit prototype

from qiskit import QuantumCircuit

qc = QuantumCircuit(2)
qc.h(0)
qc.cx(0,1)
qc.measure_all()

# serialize and hand to orchestrator
circuit_payload = qc.qasm()

GitHub Actions: matrix for simulator and mocked tests (snippet)

name: PoC CI
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      - name: Install deps
        run: pip install -r requirements.txt
      - name: Run unit tests
        run: pytest -q
      - name: Run smoke (simulator)
        run: python ci/smoke_simulator.py

Validation strategy: what to measure

Define three classes of metrics early:

Functional correctness — simulation vs expected state / observable values
Hardware delta — difference between simulator and QPU results, with confidence intervals
Operational metrics — end‑to‑end latency, cost per run, on‑device inference time

Present these to stakeholders as a simple table and a short chart showing variance over multiple runs. Stakeholders care most about predictability, cost, and integration risk — make those explicit.

Using autonomous dev tools effectively

Autonomous agents in 2026 can significantly speed up scaffolding and repetitive work, but treat them as smart copilots rather than full owners. Use them to:

Generate initial code templates and test cases
Create CI workflows and infrastructure‑as‑code snippets
Summarize logs and produce concise failure reports after CI runs

Ensure a human reviews all code and tests. Keep the agent's prompts and outputs in the repo for traceability.

Risk management and cost controls

Use simulator mode for most CI runs; reserve cloud QPU runs for nightly or scheduled smoke tests
Limit shot counts during development to control cost
Log API usage and set hard budgets/alerts for cloud quantum spend
Mock external dependencies in unit tests to avoid accidental cloud calls

Demo script — keep it tight and measurable

Intro (30s): objective and success metric
Architecture (30s): show Pi + AI HAT+ and cloud backend flow
Live run (90s): trigger preprocessing on Pi, submit to cloud via orchestrator, show result aggregation
Results (60s): show metric dashboard and simulation vs hardware delta
Next steps (30s): scaling plan and required resources

Always offer a recording of the live QPU run for reliability; stakeholders expect reproducibility.

Advanced strategies & scaling paths beyond the PoC

Parameter sweep automation: use the Pi to schedule parameter grid jobs and aggregate posterior distributions
Edge caching: cache frequent preprocessing results on Pi to reduce runtime variance and cost
Policy for multi‑provider execution: compare results across backends and pick the best provider per workload
Model distillation to move heavier preprocessing onto AI HAT+ for real‑time demos

Case study (compact): hybrid classifier PoC in 10 days

Team: 2 devs. Goal: show a hybrid classical‑quantum classifier where the Pi runs a compact feature encoder and the quantum circuit evaluates a kernel. Timeline condensed to 10 active days:

Day 1–2: scaffold and prototype circuit
Day 3–4: implement on‑device encoder (AI HAT+), benchmark
Day 5–6: integrate cloud runs, collect results
Day 7–8: automate CI and validation; run nightly smoke tests
Day 9–10: stakeholder demo and next steps

Outcome: clear delta between baseline classical model and hybrid model, with reproducible CI checks. The small scope and tight metrics made the PoC persuasive.

Common pitfalls and how to avoid them

Too broad a scope — pick one measurable metric and optimize for it.
Skipping mocks — always include mocked tests to prevent costly accidental cloud runs.
Relying solely on autonomous agents — a human must validate CI, security, and model choices.
Ignoring calibration data — for honest comparisons, store backend calibration metadata with each run.

Actionable takeaways

Start small: define one measurable success metric and aim to prove or disprove it in two weeks.
Use the Raspberry Pi 5 + AI HAT+ as an orchestrator and near‑edge preprocessing unit — it reduces demo friction and shows practical integration pathways.
Leverage autonomous dev assistants to scaffold, not to replace human review — they cut setup time dramatically.
Implement CI with mocked and scheduled cloud runs to maintain reproducibility and control costs.
Prepare a tight demo script focused on metrics and reproducibility for stakeholder buy‑in.

Final thoughts and future predictions (2026)

In 2026, expect more edge devices to include dedicated ML accelerators, and autonomous developer tools to move from scaffolding to more capable orchestrators that can run closed‑loop experiments under human supervision. The winning teams will be those that translate these capabilities into small, measurable PoCs that demonstrate integration risk and business value — not vague promises.

Call to action

Ready to run your first two‑week quantum PoC? Start by forking the reference repo in this playbook, provision a Raspberry Pi 5 + AI HAT+, and run the Day 1 scaffolding. If you want a guided walkthrough or a workshop for your team, contact our engineering mentors to shape a 2‑day hands‑on lab that finishes with a stakeholder demo.

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