Creating Reproducible Notebooks for OLAP Analysis of Qubit Calibration Data

Hook: Stop chasing calibration mysteries — make your pulse dumps queryable, reproducible, and CI-verified

If you manage qubit fleets, you know the pain: massive pulse-level calibration data, opaque CSVs from different vendors, and ad-hoc scripts that decode a day's worth of drift only for the insight to vanish when someone else runs the notebook. In 2026, teams need an industrial, reproducible way to ingest pulse-level calibration data into an OLAP store, slice it multi-dimensionally, detect drift, and publish results with continuous integration checks. This article gives a concrete notebook recipe using ClickHouse as the OLAP engine, pragmatic schema and ingestion guidance, OLAP queries for robust drift analysis, and a CI pattern to guarantee reproducibility at every commit.

Why this matters in 2026

Two important platform trends define the moment:

• ClickHouse matured rapidly: after large funding rounds in 2024–2025 and expanding enterprise usage, it's now a default choice for high-cardinality, sub-second OLAP workloads across observability and telemetry domains (see Bloomberg coverage of ClickHouse's late-2025 growth).
• Quantum calibration data has scaled from per-experiment logs to continuous pulse-level telemetry: labs produce gigabytes-to-terabytes of dumps per week, and the analytics problem now mirrors time-series observability rather than traditional lab notebooks.

Marrying ClickHouse's OLAP strengths with reproducible notebooks gives you a practical way to track qubit drift, correlate with environment telemetry (temperature, fridge pressure), and produce publishable, version-controlled insights.

Overview: Recipe at a glance

1. Design a ClickHouse schema for pulse-level calibration dumps and metadata.
2. Ingest pulse dumps (JSONL / Parquet) into ClickHouse via bulk import or Kafka pipelines.
3. Build materialized views and rollups for fast OLAP exploration.
4. Create a reproducible notebook (parametrized with papermill) that runs the queries, visualizes drift, and stores outputs.
5. Wire CI (GitHub Actions or GitLab CI) with nbval/papermill checks and dataset checksums to guarantee reproducible outputs before publication.

1) Schema: model pulse-level calibration data for OLAP

Design the ClickHouse table with OLAP queries in mind: high-cardinality dimensions (qubit_id, backend, pulse_type), numeric metrics (amplitude, frequency, measured_phase, fidelity_estimate), and time-based partitioning for efficient TTL and rollups.

CREATE TABLE qubit_pulses
(
    event_time DateTime64(6) MATERIALIZED now64(), -- timestamp of sample
    backend_id String,
    backend_version String,
    qubit_id UInt32,
    pulse_id String,
    pulse_type String,          -- e.g. X90, X180, readout
    amplitude Float64,
    frequency Float64,
    phase Float64,
    duration_us Float32,
    measured_I Float64,
    measured_Q Float64,
    fidelity_est Float32,      -- estimator from the calibration run
    temperature Float32,       -- fridge temperature at sample
    metadata JSON,             -- vendor-specific fields
    job_id String,
    sample_rate_khz UInt32
)
ENGINE = ReplacingMergeTree(job_id)
PARTITION BY toYYYYMM(event_time)
ORDER BY (qubit_id, pulse_type, event_time)
TTL event_time + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Notes: ReplacingMergeTree lets you update by job_id for corrected dumps. Partition by month for manageable compaction. TTL ensures old raw dumps are pruned while you keep derived rollups.

Schema extensions for scale

• Use CollapsingMergeTree if your ingestion pipeline may produce duplicates that need collapse rules.
• For ultra-high rate ingestion, write to a Kafka engine or a buffer table and then merge into the MergeTree table.
• Store raw waveform samples externally (object store) and keep pointers in metadata to avoid exploding table sizes — treat these pointers like artifacts in a reproducible pipeline and publish references to your artifact store such as a verified S3 location used by edge content teams.

2) Ingesting pulse-level dumps: practical patterns

Pulse dumps typically arrive as JSONL, Parquet, or vendor-specific binary blobs. Choose an ingestion strategy based on throughput and latency needs.

Batch import (suitable for nightly or ad-hoc dumps)

1. Convert vendor dumps to Parquet or JSONL using a small Python script.
2. Use clickhouse-client or clickhouse-local to bulk insert.

# Python: convert and push via clickhouse-connect
from clickhouse_connect import get_client
import pyarrow.parquet as pq

client = get_client(host='clickhouse.local', port=9440, database='quantum')

# load a parquet file with pulse-level rows
table = pq.read_table('pulse_dump_2026-01-10.parquet')
rows = table.to_pydict()

client.insert('qubit_pulses', rows)

Streaming ingestion (near real-time)

Push messages into Kafka (topic: qubit.pulses) and use ClickHouse's Kafka table engine plus a materialized view to insert into MergeTree. This scales to many devices and avoids large reloads.

3) OLAP queries to find drift patterns

Once the data is in ClickHouse, use multi-dimensional OLAP queries to detect drift across time, qubit groups, and external telemetry. Below are patterns that work in production.

Compute per-qubit rolling mean and slope (trend)

SELECT
  qubit_id,
  toStartOfInterval(event_time, INTERVAL 1 HOUR) as hour,
  avg(amplitude) AS mean_amp,
  quantileTDigest(0.5)(fidelity_est) as median_fid
FROM qubit_pulses
WHERE event_time >= now() - INTERVAL 30 DAY
GROUP BY qubit_id, hour
ORDER BY qubit_id, hour;

For slope (drift rate) compute linear regression over a window. ClickHouse provides simple window functions and aggregate functions; if you need more complex models, export aggregated time-series to Python or run model inference via a deployed ML endpoint.

Detect abrupt changes using EWMA and z-score

SELECT
  qubit_id,
  hour,
  mean_amp,
  (mean_amp - avg(mean_amp) OVER (PARTITION BY qubit_id ORDER BY hour ROWS BETWEEN 24 PRECEDING AND 1 PRECEDING))
    / stddevPop(mean_amp) OVER (PARTITION BY qubit_id ORDER BY hour ROWS BETWEEN 24 PRECEDING AND 1 PRECEDING)
    AS z_score
FROM
(
  -- previous query as subselect
)
WHERE abs(z_score) > 3;

This flags hours where amplitude jumps relative to recent history. Pair z-score detection with change-point detection in a notebook for visual confirmation.

Multi-dimensional correlation: correlate drift with fridge temperature

SELECT
  qubit_id,
  corrState(mean_amp, temperature) AS corr_state
FROM
(
  SELECT
    qubit_id,
    toStartOfInterval(event_time, INTERVAL 1 HOUR) AS hour,
    avg(amplitude) AS mean_amp,
    any(temperature) AS temperature
  FROM qubit_pulses
  WHERE event_time >= now() - INTERVAL 30 DAY
  GROUP BY qubit_id, hour
)
GROUP BY qubit_id
HAVING corrMerge(corr_state) > 0.3;

ClickHouse's corrState/corrMerge lets you compute correlation across aggregated windows to find qubits whose amplitude drift correlates significantly with temperature changes.

4) Notebook pattern: reproducible, param-driven analysis

Build your analysis notebook using the following principles to ensure reproducibility and easy CI integration:

• Parameterize inputs (date range, backend_id, qubit subset) using papermill or Jupyter parameters so CI can run deterministic analyses.
• Pin environment — provide Dockerfile, conda-lock, or Nix flake that reproduces the release kernel and Python libs.
• Deterministic query snapshots — store the query text in version control and tag result hashes to detect drift between runs.
• Artifact outputs — store PNG/CSV outputs as artifacts in CI or push to an artifact store (S3) with checksums and dataset versioning (DVC or qbitshare dataset registry).

Example notebook workflow (high level)

1. Parameters: start_date, end_date, qubit_list, backend_id.
2. Run ClickHouse queries via clickhouse-connect to produce aggregates.
3. Run statistical checks (drift detection, correlation tests) in Python (pandas, scipy).
4. Generate plots (plotly/matplotlib) and export CSV summary.
5. Save run metadata (git_sha, conda_lock_hash, query_text, result_hash) to ClickHouse audit table.

# Notebook: connect and run
from clickhouse_connect import get_client
import pandas as pd

client = get_client(host='clickhouse.local', database='quantum')
query = open('queries/drift_hourly.sql').read()
params = {'start': '2026-01-01', 'end': '2026-01-15', 'backend': 'ibmq_rigetti_alpha'}
q = query.format(**params)
df = client.query_dataframe(q)
# run additional checks and plotting

5) CI: verify notebooks and data before publishing

Continuous Integration ensures that notebook outputs are reproducible, tests pass, and datasets are unchanged. Here’s a production-ready pattern using GitHub Actions (but it translates to GitLab CI, Jenkins, etc.).

CI steps

1. Checkout code and data pointers.
2. Start a reproducible environment: build Docker image from repo's Dockerfile (pinned base images) and run ephemeral sandboxed runners for notebook execution with tools like ephemeral workspaces.
3. Run a smoke test to ensure ClickHouse connectivity (or use a test ClickHouse instance in CI).
4. Execute notebooks with papermill to ensure they run end-to-end with provided params.
5. Run nbval or pytest comparisons to golden outputs: compare CSV checksums or result hashes.
6. When outputs change, fail unless a human approves updated golden files via PR.
7. Publish artifacts (plots, CSVs) to an artifact store and create a release with a digest and provenance metadata — treat artifact publication the same way content teams publish edge assets in edge publishing workflows.

Minimal GitHub Actions snippet

name: Notebook CI
on: [push, pull_request]

jobs:
  test-notebooks:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build image
        run: docker build -t qcal-notebook:ci .
      - name: Run notebooks
        run: |
          docker run --rm -v ${{ github.workspace }}:/work qcal-notebook:ci bash -lc "pip install papermill && papermill notebooks/drift_analysis.ipynb out.ipynb -p start '2026-01-01' -p end '2026-01-15'"
      - name: Compare outputs
        run: python tests/compare_outputs.py --expected artifacts/golden_summary.csv --actual out_summary.csv

6) Publish reproducible results and provenance

Publishing is more than pushing a notebook to a repo. Capture provenance so others can reproduce your entire analysis:

• Attach Docker image digest or conda-lock file to the notebook release.
• Store dataset versions in DVC or your dataset registry; don’t commit raw TBs to Git. Record checksums for pulse dumps and point to an S3/HTTP location.
• Write an audit row into a ClickHouse analysis_runs table: commit SHA, notebook name, parameters, start/end times, results checksum, and link to artifacts.

INSERT INTO analysis_runs (run_time, commit_sha, notebook, params, result_hash)
VALUES (now(), 'abc123', 'drift_analysis.ipynb', '{"start":"2026-01-01","end":"2026-01-15"}', 'sha256:...')

7) Practical examples: from query to published figure

Walkthrough: you want to show a figure of amplitude drift for qubits [1,2,3] over two weeks and assert no qubit had a >5% median amplitude drop without explainers.

Step A — Query aggregated hourly medians

SELECT qubit_id, hour, quantileTDigest(0.5)(amplitude) AS med_amp
FROM qubit_pulses
WHERE event_time BETWEEN '2026-01-01' AND '2026-01-15'
AND qubit_id IN (1,2,3)
GROUP BY qubit_id, hour
ORDER BY qubit_id, hour;

Step B — Compute percent change vs baseline in notebook

df['baseline'] = df[df.hour <= '2026-01-02'].groupby('qubit_id').med_amp.transform('median')
df['pct_change'] = (df.med_amp - df.baseline) / df.baseline * 100
alerts = df.groupby('qubit_id').pct_change.max() > 5

Step C — CI check

In CI, run the notebook and assert alerts is False. If True, fail and attach the figure and the raw aggregated CSV for triage.

8) Advanced strategies and performance tips

• Use materialized views for hourly and daily rollups to speed iterative analysis — same pattern used in high-throughput observability systems covered in edge observability.
• Leverage AggregateFunction and small in-cluster aggregations to reduce network transfer when computing heavy stats.
• For cross-qubit correlation matrices, use sampled aggregates or incremental batch processing to keep compute bounded.
• Enable compression codecs for JSON/metadata columns (e.g., LZ4) to reduce storage of high-cardinality metadata.
• Use ClickHouse's Profiles and Quota to limit runaway ad-hoc queries from notebooks in shared environments.

9) Common pitfalls and how to avoid them

• Ingesting raw waveform arrays directly into MergeTree without externalizing: leads to huge tables. Keep waveforms in object store and reference URIs.
• Unpinned notebook environments: results change across runs. Always publish an environment lockfile or Docker digest.
• Relying on ad-hoc golden files without provenance: CI updates silently. Require PR approval for golden updates and attach rationale in PR description.
• Ignoring time zone and clock skew: ensure event_time is normalized (UTC) and store device clock offsets in metadata.

2026 trends and what to expect next

As analyses scale in 2026, expect:

• More standardization around pulse-level telemetry formats (OpenPulse derivatives) and vendor adapters.
• Deeper integration of OLAP stores with model inference: running lightweight ML models close to data for anomaly scoring before exporting data to notebooks.
• Improved private cloud ClickHouse offerings making low-latency queries against large calibration datasets easier for regulated labs.

Investing early in a reproducible pipeline based on ClickHouse gives you a scalable foundation to adopt these advances without disrupting research workflows.

"Make the data queryable, make the analysis repeatable, and make the results verifiable." — Practical mantra for production-grade qubit analytics (2026)

Actionable takeaways

• Design your ClickHouse schema with partitions by time and ORDER BY on (qubit_id, pulse_type, event_time) to support efficient OLAP queries.
• Prefer storing waveform pointers externally; keep per-sample aggregates in ClickHouse for fast analytics.
• Parameterize notebooks with papermill, pin environments with Docker/conda-lock, and automate checks in CI using papermill + nbval.
• Use materialized views and rollups to reduce query latency for repeated visualization runs.
• Record provenance (commit SHA, environment digest, dataset checksums) in an audit table to make publications reproducible and verifiable.

Next steps — put this recipe into practice

If you manage calibration data today, start by:

1. Drafting the ClickHouse schema and creating a test dataset for one device.
2. Implementing a small ingestion script and verifying a few OLAP queries locally.
3. Creating a parametrized notebook and wiring a simple GitHub Actions job to run it daily and publish artifacts.

Need a starter kit? We maintain example schemas, notebook templates, and CI workflows tailored to quantum calibration analytics on qbitshare. Use them to accelerate integration with your lab and make your next publication reproducible and audit-ready.

Call to action

Ready to make your qubit calibration analytics reproducible and production-ready? Download the ClickHouse notebook starter kit on qbitshare, fork the repo, and open a PR with your first dataset. If you want hands-on help, request a reproducibility review — we’ll walk your team through schema design, ingestion, and CI checks tailored to your quantum stack.

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