Intro to Quantum Data Analytics: Visualizing Qubit Experiment Logs with ClickHouse

Turn noisy qubit logs into classroom insights: ingest, analyze and visualize with ClickHouse

Struggling to give students hands-on access to quantum experiments because hardware is scarce or logs are too messy to analyze? This beginner-friendly walkthrough shows how to ingest quantum experiment logs into an OLAP-style database (ClickHouse), run fast aggregations, and build visual dashboards your classroom can explore.

Why ClickHouse for quantum analytics in 2026?

ClickHouse has become a de-facto choice for interactive, high-cardinality analytics across telemetry and experiment logs. In early 2026 ClickHouse closed a large funding round and continues rapid feature development, making it easier to host and query large time-series datasets at classroom scale. For educators and makers, the combination of columnar storage, fast vectorized queries, and simple ingestion formats means students spend time learning analysis—not waiting on queries.

ClickHouse raised a major funding round in early 2026, accelerating adoption in analytics and telemetry use cases (Bloomberg, Jan 2026).

What you’ll build in this tutorial

By the end of this article you will have:

• Designed a ClickHouse schema for quantum experiment logs
• Ingested sample logs (CSV/JSON) from a qubit experiment pipeline
• Queried and aggregated results with OLAP-style SQL for classroom exercises
• Built quick visualizations using Python + Plotly (or Grafana/Superset)
• Learned debugging and performance tips for bulk loads and JOINs

Step 1 — Define a practical schema for qubit experiment logs

Keep the schema simple but expressive. For classroom datasets you want to answer time-series, per-qubit, and per-circuit queries. The following schema balances storage efficiency and query flexibility.

CREATE TABLE qubit_logs (
  event_time DateTime64(6), -- precise timestamp
  experiment_id String,
  circuit_name String,
  backend String,
  shot UInt32,              -- shot number
  qubit_count UInt8,
  expected_bitstring String,
  measured_bitstring String,
  measured Array(UInt8),    -- parsed array of 0/1
  fidelity Float32,         -- estimated fidelity metric
  noise_level Float32,      -- estimated noise metric
  tags Array(String)
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_time)
ORDER BY (experiment_id, event_time, shot)
SETTINGS index_granularity = 8192;

Notes: Use MergeTree for OLAP workloads. Partition by month (toYYYYMM) for time-chunked pruning. Order by experiment_id + event_time gives fast scans for per-experiment time-series.

Why store both bitstrings and arrays?

String bitfields are compact and human-readable for exports; Array(UInt8) (0/1) enables vectorized aggregation and per-qubit heatmaps. You can ingest both, then parse the array as a lightweight transform.

Step 2 — Prepare a sample dataset (classroom-friendly)

For teaching, generate or use a small dataset (10k–200k rows) containing metadata and raw measurement outcomes. If you have Qiskit logs, export to JSONEachRow. Otherwise, create a synthetic CSV with columns matching the schema above. Example rows:

event_time,experiment_id,circuit_name,backend,shot,qubit_count,expected_bitstring,measured_bitstring,fidelity,noise_level,tags
2026-01-10T12:00:00.123456,exp-01,bell-pair,local-sim,1,2,00,01,0.98,0.02,"['demo','classroom']"
2026-01-10T12:00:00.123789,exp-01,bell-pair,local-sim,2,2,00,00,0.97,0.03,"['demo','classroom']"

Step 3 — Ingest logs into ClickHouse

There are multiple ways to ingest: clickhouse-client, HTTP interface, or Python. For classroom reproducibility we’ll use the Python clickhouse-driver and batch INSERTs.

from clickhouse_driver import Client
import csv

client = Client('localhost')

with open('qubit_logs.csv', 'r') as f:
    reader = csv.DictReader(f)
    batch = []
    for row in reader:
        measured_arr = [int(c) for c in row['measured_bitstring']]  # parse '01' -> [0,1]
        batch.append((
            row['event_time'], row['experiment_id'], row['circuit_name'], row['backend'],
            int(row['shot']), int(row['qubit_count']), row['expected_bitstring'], row['measured_bitstring'],
            measured_arr, float(row['fidelity']), float(row['noise_level']), row.get('tags', '[]')
        ))
        if len(batch) >= 1000:
            client.execute('INSERT INTO qubit_logs VALUES', batch)
            batch = []
    if batch:
        client.execute('INSERT INTO qubit_logs VALUES', batch)

Tips: Use batches of 500–50,000 rows depending on memory. For very large datasets prefer ClickHouse's HTTP/CSV bulk upload or Kafka pipeline.

Step 4 — OLAP-style queries your students will love

Now the fun part: interactive aggregations teachers use in short labs.

1) Time-series: daily shot counts and error rates

SELECT
  toDate(event_time) AS date,
  count() AS shots,
  countIf(measured_bitstring != expected_bitstring) AS errors,
  round(errors / shots, 6) AS error_rate
FROM qubit_logs
WHERE experiment_id = 'exp-01'
GROUP BY date
ORDER BY date;

2) Per-qubit error heatmap (convert Array(UInt8))

SELECT
  pos AS qubit_index,
  sum(value) AS errors,
  count() AS shots,
  errors / shots AS error_rate
FROM qubit_logs
ARRAY JOIN arrayEnumerate(measured) AS pos, measured[pos] AS value
WHERE experiment_id = 'exp-01'
GROUP BY pos
ORDER BY pos;

3) Confusion matrix of expected vs measured (small qubit counts)

SELECT expected_bitstring, measured_bitstring, count() AS cnt
FROM qubit_logs
WHERE qubit_count <= 6
GROUP BY expected_bitstring, measured_bitstring
ORDER BY cnt DESC
LIMIT 100;

These queries run quickly even on modest hardware because ClickHouse reads columnar data for specific fields, skipping unrelated columns.

Step 5 — Visualize: Python + Plotly example

Use the clickhouse-driver to fetch the time-series and plot it via Plotly for interactive classroom notebooks.

from clickhouse_driver import Client
import pandas as pd
import plotly.express as px

client = Client('localhost')
rows = client.execute("""
SELECT toDate(event_time) AS date, count() AS shots,
  countIf(measured_bitstring != expected_bitstring) AS errors
FROM qubit_logs
WHERE experiment_id = 'exp-01' AND event_time >= '2026-01-01'
GROUP BY date
ORDER BY date
""")

df = pd.DataFrame(rows, columns=['date','shots','errors'])
df['error_rate'] = df['errors'] / df['shots']
fig = px.line(df, x='date', y='error_rate', title='Daily error rate for exp-01')
fig.show()

Alternative: Use Grafana with the ClickHouse datasource or Apache Superset for no-code dashboards—great for showing results to non-coders.

Step 6 — Classroom exercises and project ideas

1. Ingest logs from 3 different backends (simulator, local noisy device, remote cloud) and compare error rates.
2. Build a per-qubit heatmap for a large experiment and identify the noisiest qubit.
3. Create a materialized view that stores daily summaries (shots, errors) and connect it to Grafana for live dashboards.
4. Use SQL to compute a confusion matrix, then implement a simple correction strategy and re-run error-rate queries to show improvement.
5. Challenge: Use ClickHouse's approximate distinct or quantile functions to estimate distribution of fidelities at scale.

Advanced strategies & 2026 trends to leverage

As of 2026, trends to consider when planning analytics workflows:

• Edge and hybrid analytics: Many quantum kits now generate local logs. Use ClickHouse in a lightweight VM or single-node Docker for local classroom use.
• Vectorized and hardware-accelerated analytics: Recent ClickHouse releases have improved vectorized execution and >performance for array operations—use Array functions for per-qubit analysis.
• Integration with Arrow and ML pipelines: Export query results via Apache Arrow for direct consumption in scikit-learn or PyTorch for simple error-mitigation experiments.
• Governed datasets: Teach reproducibility by snapshotting a dataset (to Parquet) and ingesting into ClickHouse using the HTTP interface for deterministic labs.

Debugging & performance tips

• Type mismatches: Common when importing CSV. Check DateTime parsing and ensure arrays are inserted as ClickHouse arrays or parse them server-side with functions like splitByChar.
• Batch sizes: Smaller batches reduce memory pressure; larger batches reduce overhead. For classroom VM use 1k–10k rows per batch.
• Indexing: ClickHouse uses primary key ordering for range scans. For frequent per-experiment queries, include experiment_id in ORDER BY.
• Compression & storage: ClickHouse compresses columns; store measured arrays in their own column to benefit from compression if patterns repeat.
• Materialized views: Use these for pre-aggregated daily metrics to speed dashboards; they also make instructor demos instantaneous.

Example materialized view: daily_error_rates

CREATE TABLE daily_error_rates (
  dt Date,
  experiment_id String,
  shots UInt64,
  errors UInt64,
  error_rate Float32
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(dt)
ORDER BY (experiment_id, dt);

CREATE MATERIALIZED VIEW mv_daily_error_rates TO daily_error_rates AS
SELECT toDate(event_time) AS dt,
  experiment_id,
  count() AS shots,
  countIf(measured_bitstring != expected_bitstring) AS errors,
  round(errors / shots, 6) AS error_rate
FROM qubit_logs
GROUP BY dt, experiment_id;

Real-world classroom case study (compact)

At a university lab in late 2025 we used a single-node ClickHouse VM to collect 150k shots from student-run qubit experiments across three days. Students could run queries in a Jupyter notebook and visualize error heatmaps within minutes. The instructor created a materialized view for daily error rates so the first lecture could demonstrate statistical trends without waiting for raw aggregation.

Key wins: reduced friction (students focused on interpretation), reproducibility (snapshotted datasets), and speed (sub-second queries for summary stats).

Common pitfalls and how to avoid them

• Not normalizing expected vs measured strings: store expected_bitstring as canonical padded strings to avoid grouping errors.
• Over-partitioning: too many small partitions (daily) can degrade insert performance; monthly partitioning is a good middle-ground for most classroom datasets.
• Unbounded cardinality in tags: store a finite set of tags or normalize tag table for metadata joins.

Actionable takeaways

• Design tables to support both time-series and per-qubit analysis (strings + arrays).
• Use batch INSERTs and partitioning to keep query latency low in classroom settings.
• Leverage materialized views to make dashboards instant and deterministic for demos.
• Bring visualization into notebooks (Plotly) or connect Grafana for easy dashboards your students can interact with.

Next steps & resources

To reproduce this workflow in your course:

1. Install ClickHouse locally (Docker images work great for class machines).
2. Prepare a synthetic or exported dataset from Qiskit/other SDKs.
3. Run the schema and ingestion samples above.
4. Build a simple notebook and one dashboard panel for the class demo.

If you want a ready-to-run starter kit, check the accompanying classroom repo (notebook + sample CSV + Grafana dashboard) we maintain. It contains the scripts used in this article so you can deploy a lab in under an hour.

Final thoughts: why quantum analytics matters in 2026

As quantum hardware becomes more available to universities and makers, the bottleneck is no longer just access—it’s making experiment data meaningful. ClickHouse and modern OLAP techniques let educators turn raw logs into real learning moments: time-series that reveal noise trends, per-qubit visualisations that inspire debugging, and reproducible datasets for student projects.

Start small: one experiment, one table, and a single dashboard panel. Students will quickly move from observing noise to hypothesizing mitigation strategies—and that’s where real learning happens.

Call to action

Ready to run a live lab? Download the starter dataset and notebook from our GitHub (search boxqubit-classroom-clickhouse), spin up a ClickHouse Docker container, and follow the scripts above. If you want a tailored classroom kit or step-by-step slides for a 90-minute lab, sign up for our educator bundle and get ready-to-run resources, example datasets, and instructor notes.