Open-Source Dashboard Templates for Quantum Micro-Apps — Starter Kit

Hook: Stop wrestling with ad-hoc quantum tools — ship reproducible micro-apps your whole lab can use

Pain point: experiments scattered across notebooks, bulky datasets stuck on lab drives, and dashboards only devs can maintain. In 2026 those problems slow research and fracture collaboration. This starter kit of open-source micro-app dashboard templates (experiment scheduler, calibration reporter, dataset browser) is built so non-developers can customize, deploy, and share—fast.

Why micro-app dashboards matter for quantum teams in 2026

By late 2025 and into 2026, hybrid classical–quantum workflows and multi-cloud quantum runtimes became the norm. Research teams now run mixed backends (simulators, noisy hardware, multiple cloud providers) and need lightweight, reproducible front-ends to coordinate experiments and share artifacts.

Micro-apps are bite-sized web apps focused on one job. They reduce cognitive load, fit existing CI/CD pipelines, and—critically—allow non-developers (lab managers, experimental physicists, data stewards) to own the UX for running and sharing quantum work. This starter kit emphasizes no-code customization and community contribution so micro-apps grow with your lab, not against it.

What you get in the Starter Kit

Three opinionated templates designed for reproducible quantum research:

• Experiment Scheduler — queue, prioritize, and monitor experiments across simulators and hardware.
• Calibration Reporter — collect, visualize, and compare calibration runs over time with drift detection.
• Dataset Browser — searchable, versioned datasets with preview, checksum verification, and secure transfer buttons.

Each template includes:

• Prebuilt UI components (no-code config panels)
• Lightweight backend adapters (REST + GraphQL endpoints)
• Connectors for common quantum SDKs (Qiskit, Cirq, Amazon Braket, and a generic REST executor)
• Example GitHub repository with CI/CD workflows, GitHub Pages and Docker deployment options
• Short walkthroughs for non-developers and contributor guides
• Community badges for quick trust signals and discoverability

Design principles — why these templates work for non-developers

• Config-first: Change behavior via a YAML/JSON config file or a simple visual form—no JS editing required.
• Composable: Mix and match components (schedulers, graphs, file viewers) without changing core code.
• Provenance-aware: Built-in metadata capture (commit SHA, dataset checksum, runtime tags) to enforce reproducibility.
• Cloud-agnostic: Adapter pattern supports local simulators, on-prem hardware, and major cloud providers.
• Community-first: Badges, contributor guides, and extension points encourage shared workflows and curated templates.

How the templates are structured (architecture overview)

The micro-apps use a minimal, understandable stack so non-developers can deploy quickly and maintain ownership.

• Frontend: Static SPA (SvelteKit/React) served via GitHub Pages, Netlify or Vercel. Configuration panels render forms from JSON Schema so site managers can tweak behavior in the UI.
• Backend: Optional light API (FastAPI/Express) that handles authentication, experiment queueing, and dataset metadata. Can run as a single Docker container or be replaced with serverless endpoints.
• Adapters: Small connector modules to Qiskit, Cirq, Braket, or a generic REST job runner. Each adapter maps a micro-app command to a provider API and follows patterns similar to running community experiments in projects like running quantum simulators locally.
• Storage: Git + Git LFS / DVC for small teams; S3-compatible or Globus for larger dataset transfer needs. Built-in checksum and provenance metadata.
• CI/CD: GitHub Actions included for test runs, badge updates, and deployment. Actions are written so non-devs can toggle them via repository settings.

Walkthrough 1 — Experiment Scheduler (5–15 minute no-code setup)

Goal

Allow a lab manager to queue parameterized quantum experiments, set priorities, and view live status across backends.

Step-by-step

1. Fork the scheduler template repository and open config/scheduler.yaml in the repo root.
2. Use the visual form (Site Settings > Scheduler Config) to add backends. For each backend, enter a name, endpoint, and auth token.
3. Define a set of experiment templates (parameters, circuit files, and expected outputs). Choose a default experiment priority.
4. Enable notifications (Slack/Teams/Email) by toggling the integration and pasting your webhook URL — no code required.
5. Deploy: click the included Deploy button to build to Netlify or run a single Docker command locally.

Minimal scheduler YAML example (editable in the UI):

backends:
  - name: qpu-east
    type: braket
    endpoint: https://quantum.example.com/api
    auth: ${BRACKET_TOKEN}
  - name: local-sim
    type: simulator
    endpoint: http://localhost:8001
templates:
  - id: bell-test
    description: Run Bell-state fidelity sweep
    params:
      shots: {type: integer, default: 1024}
      noise_level: {type: number, default: 0.01}
default_priority: medium
notifications:
  slack: ${SLACK_WEBHOOK}

What non-developers will love

• Visual queue management and drag-drop priority ordering
• One-click rerun from a historical entry with preserved provenance
• Prebuilt adapters mean you rarely touch code—swap endpoints in the UI

Walkthrough 2 — Calibration Reporter (10–20 minutes)

Goal

Track calibration parameters (T1, T2, readout error) across devices and automatically flag drift.

Step-by-step

1. Fork the calibration reporter and open the Visual Config.
2. Add calibration metrics you collect (names, units, acceptable ranges). The UI will render forms and charts automatically.
3. Point the reporter at your calibration dataset store (S3 path, GitHub repo, or local path). It will index metadata and compute baselines.
4. Enable automated weekly calibration comparisons and alerting where metrics exceed thresholds.

Automated drift detection runs as a scheduled GitHub Action in the starter repo. Example rule snippet:

drift_rules:
  - metric: t1
    threshold_pct: 15
    window_days: 14
    alert: true

Advanced tip

Combine calibration reporter outputs with the scheduler to deprioritize noisy hardware automatically. Use the simple rule-mapping UI to link calibration flags to scheduler backends.

Walkthrough 3 — Dataset Browser (15–30 minutes)

Goal

Provide your team a searchable catalog of datasets with previews, checksums for integrity, and secure transfer buttons for heavy files.

Step-by-step

1. Fork the dataset browser; open data/index.json or connect to your dataset registry (DVC remote, S3 bucket, or QubitShare host).
2. Use the visual editor to add metadata fields (experiment, date, device, tags). Metadata is stored as JSON-LD to support indexing and semantic search powered by modern discovery patterns like those described in AI-powered discovery.
3. Enable preview generators: waveform images, histogram snapshots, and notebook previews (nbviewer integration) for quick inspection.
4. Activate secure transfer using a preconfigured Globus or S3 presigned URL integration to allow colleagues to download large artifacts safely.

Metadata example stored with the dataset (JSON-LD):

{
  "@context": "https://schema.org/",
  "@type": "Dataset",
  "name": "Bell State Sweep - Device X",
  "description": "Parameter sweep results",
  "url": "https://datasets.example.com/bell-sweep",
  "checksum": "sha256:abcd1234...",
  "version": "v1.3",
  "tags": ["bell", "fidelity", "device-x"]
}

Community badges — trust signals that non-devs can add in minutes

Badges improve discoverability and reduce friction for adoption. Use them to show dataset verification, reproducible runs, template compliance, and community status.

Examples using Shields.io (paste in README or site footer):

• Verified dataset:
• Reproducible:
• Community template:

Automate badge updates with a GitHub Action that runs dataset checks and updates the badge JSON. Example workflow snippet:

name: Update Dataset Badge
on:
  push:
    paths:
      - 'datasets/**'
jobs:
  badge:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run dataset checker
        run: python tools/check_dataset.py --path datasets/bell-sweep
      - name: Upload badge
        uses: actions/upload-artifact@v4

Real-world examples and case studies (experience-driven wins)

Example: A 6-lab consortium used the Starter Kit to standardize experiment submission. Within two months they reduced queue friction by 40% and reduced sample turnaround from 3 days to 1.5 days by leveraging the scheduler plus automated calibration-based routing.

Example: An academic group replaced a custom Jupyter-only workflow with the Dataset Browser and regained trust from collaborators by publishing checksummed datasets and adding reproducible badges. Their shared dataset downloads rose by 300% and collaboration requests increased.

“We went from 'who ran that?' to 'here's the exact commit and dataset' — reproducibility became visible.” — Lab Manager, Multi-Institution Project (2025 trial)

Advanced strategies for scaling and governance

1. Versioning and provenance

Use Git + Git LFS or DVC for code and smaller artifacts; for larger bits use S3/Globus with metadata stored in Git. Always capture the following metadata for an experiment run: commit SHA, adapter version, backend id, dataset checksum, and runtime environment (container image tag).

2. Access control and auditability

Secure micro-app backends with OAuth2 or short-lived tokens. For dataset access, prefer presigned URLs or managed transfer services that support audit logs (e.g., Globus or cloud provider transfer logs). For guidance on audit trails and best practices, see audit trail best practices.

3. CI for experiments

Use CI pipelines to run small smoke tests against your adapters. Example: a GitHub Action that runs a tiny simulator job on pull requests to verify a new experiment template won't break the scheduler. For patterns on cloud pipeline scaling and test runs, check this cloud pipelines case study.

4. Observability and metrics

Expose metrics (queue depth, job latency, calibration drift rates) via Prometheus-compatible endpoints. Dashboards are pluggable into the micro-apps so non-devs can observe trends without diving into logs. Ops teams can pair this with zero-downtime testing and local tunnels described in hosted tunnels and local testing.

Integration tips with popular quantum SDKs (pragmatic connector recipes)

• Qiskit: Use the adapter to build a minimal executor that accepts QASM or transpiled circuits and returns job IDs and results. Keep credentials in GitHub Secrets.
• Cirq: Ship serialized circuits as protobuf or JSON and replay with the adapter to local simulators for CI smoke tests.
• Amazon Braket / Azure Quantum: Use provider APIs via the adapter and capture the provider job ARN in the run metadata.

Practical customization patterns for non-developers

1. Start with a single template and the visual config — change colors, labels, and default parameters from the UI.
2. Onboard one power user (lab manager) to maintain config changes. That user handles repo PRs for metadata only.
3. Use prebuilt connectors to add a new backend: fill in the adapter form, verify with a smoke run, then toggle it live.
4. Encourage small contributions via community badges and a CONTRIBUTING.md that explains how to add templates or dataset entries. For ideas about distribution and discoverability, see the docu-distribution playbook.

Future-proofing: trends to expect in 2026 and beyond

Here are the key trends you should plan for in 2026:

• AI-assisted micro-app customization: Tools will generate UI forms from dataset schemas automatically and propose scheduler rules based on historical runs.
• Edge-class simulators: Lightweight on-prem simulators reduce cloud costs and increase privacy for sensitive research artifacts.
• Inter-lab federated discovery: Shared index services will let collaborators discover reproducible datasets across institutions while respecting access controls.

Adopt templates that are modular and maintain clear extension points so you can plug in these emerging capabilities without a rewrite.

Contributor guide — how to add your micro-app or dataset

1. Fork the starter-kit repository and choose an open issue labeled "good-first-template".
2. Follow the template spec (config schema and metadata shape) in /docs/spec.md.
3. Add your micro-app as a subdirectory and include a sample config + screenshots or GIFs in the README.
4. Open a pull request and request a community-reviewer badge. Template merges that meet the spec receive a "Community Template" badge automatically.

Actionable takeaways — start today

• Clone the starter kit and deploy one template this week—use the visual config to connect a single backend.
• Publish one dataset with checksum and a reproducible badge to build trust immediately.
• Identify a non-developer power user to own configs and community onboarding.

Closing — join the community and ship reproducible quantum micro-apps

Micro-app templates bridge the gap between deep quantum expertise and everyday usability. In 2026 the teams that standardize on small, reproducible dashboards and empower non-developers to own them will run experiments faster, share results easier, and build stronger multi-institution collaborations.

Ready to get started? Download the Open-Source Dashboard Templates for Quantum Micro-Apps — Starter Kit, follow the short walkthroughs, and add community badges to your repositories. Contribute templates, share use-cases, and claim contributor badges so other teams can reuse your patterns.

Call to action: Fork the starter kit on GitHub, deploy a micro-app this week, and post your first verified dataset to the community forum to earn your first contributor badge.

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