tutorialClickHousequantum

Hands-on: Integrating Quantum Simulators with ClickHouse for Large-scale Logging

aaskqbit

2026-02-12

10 min read

A practical 2026 tutorial: stream quantum simulator telemetry into ClickHouse, design qubit-centric schemas, build ETL and dashboards for experiment observability.

Hook: You're running large quantum simulator campaigns — now get observability that scales

Running hundreds or thousands of simulator jobs to tune error mitigation, gate schedules, or noise models creates a mountain of telemetry: per-shot bitstrings, statevector snapshots, per-gate timings, and custom metrics. Traditional logging approaches break down: files are hard to query, ad-hoc CSVs don't support real-time aggregation, and dashboards are slow. This tutorial shows a practical, production-ready pattern for streaming quantum simulator outputs into ClickHouse, designing schemas for qubit metrics, and building dashboards to surface experiment insights at scale.

Why ClickHouse for quantum simulator logging in 2026?

ClickHouse has moved from niche OLAP to mainstream observability and analytics. After its growth and major funding in late 2025, ClickHouse is now widely used for high-cardinality, time-series, and telemetry workloads — exactly the properties of simulator output. Key advantages for quantum teams:

High ingest throughput: supports bulk inserts, Kafka engine integration, and fast HTTP writes for streaming experiments.
Columnar storage: efficient aggregations over qubit-level metrics and large shot counts.
Materialized views & TTL: compute real-time summaries and automatically drop old raw data cheaply.
Integration ecosystem: Grafana, Superset, and native SQL make building dashboards straightforward.

Overview of the pattern

Simulate experiments with a quantum simulator (e.g., Qiskit Aer, PennyLane + NumPy/CuQuantum, or Qulacs).
Emit structured telemetry per-shot, per-gate, and per-run.
Stream logs to ClickHouse directly via HTTP/binary client or through Kafka for durability.
Use ClickHouse schema patterns (MergeTree, partitioning, materialized views) to optimize queries.
Build dashboards (Grafana / Superset) and create ETL jobs for aggregated analytics.

Designing a schema for qubit metrics

Before piping data, design a schema that balances write efficiency and query flexibility. We recommend a two-table strategy:

raw_events — high-cardinality, write-optimized table storing per-shot and per-gate records.
summary_metrics — aggregated, read-optimized table with daily/hourly summaries and per-qubit aggregates.

raw_events schema (recommended)

CREATE TABLE IF NOT EXISTS quantum.raw_events (
    ts DateTime64(6),            -- event timestamp (UTC)
    experiment_id String,        -- stable id for the experiment
    run_id String,               -- individual job/run id
    event_type String,           -- 'shot', 'gate', 'statevector', 'metric'
    qubit_index UInt16,         -- optional: which qubit this event concerns
    shot_index UInt32,          -- optional: shot number for 'shot' events
    bitstring String,           -- measurement outcome for shots
    amplitude Float64,          -- optional: amplitude or fidelity metric
    gate_name String,           -- for gate events (e.g., 'cx', 'rz')
    gate_duration Float32,      -- microseconds
    error_rate Float32,         -- if computed by simulator/noise model
    metadata String              -- JSON dictionary for extensibility
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(ts)
  ORDER BY (experiment_id, run_id, ts)
  SETTINGS index_granularity = 8192;

Notes:

Use DateTime64(6) for microsecond precision, useful for gate timings.
Partition by month (or day for very high ingest) to keep pruning and compaction efficient.
Keep a generic metadata JSON field for simulator-specific details (backend, noise model, seed).

summary_metrics schema (materialized view target)

CREATE TABLE IF NOT EXISTS quantum.summary_metrics (
    ts_hour DateTime64(6),
    experiment_id String,
    run_id String,
    qubit_index UInt16,
    avg_error_rate Float32,
    median_gate_duration Float32,
    shots UInt32,
    fidelity Float32
  ) ENGINE = MergeTree()
  PARTITION BY toYYYYMM(ts_hour)
  ORDER BY (experiment_id, run_id, qubit_index, ts_hour);

We'll populate summary_metrics via a materialized view that aggregates writes to raw_events. This gives near-real-time summaries without expensive ad-hoc queries.

Streaming patterns: Direct vs Kafka-backed

Two production patterns work well:

Direct HTTP / native client writes — simplest for small-to-medium pipelines. Use batching and the ClickHouse HTTP insert API or a native driver like clickhouse-connect.
Kafka → ClickHouse — recommended for large-scale, decoupled pipelines. Simulators push JSON events to Kafka; ClickHouse consumes via the Kafka table engine and a materialized view writes to MergeTree.

Example: pushing from a Python simulator to ClickHouse (direct)

This example uses Qiskit Aer for clarity, but the pattern works with any simulator. We'll stream per-shot JSON rows via HTTP in batches.

import time
import json
import requests
from qiskit import QuantumCircuit, Aer, transpile

CH_WRITE_URL = 'http://clickhouse.local:8123/?database=quantum&query=INSERT%20INTO%20raw_events%20FORMAT%20JSONEachRow'

sim = Aer.get_backend('aer_simulator')
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure_all()

qc = transpile(qc, sim)
job = sim.run(qc, shots=1024)
result = job.result()
counts = result.get_counts()

batch = []
now = time.time()
for bitstring, count in counts.items():
    for shot_idx in range(count):
        row = {
            'ts': now,
            'experiment_id': 'exp-123',
            'run_id': 'run-456',
            'event_type': 'shot',
            'bitstring': bitstring,
            'shot_index': shot_idx,
            'metadata': json.dumps({'backend': 'aer_simulator', 'shots': 1024})
        }
        batch.append(json.dumps(row))

# send in one request (JSONEachRow requires newline-delimited JSON)
resp = requests.post(CH_WRITE_URL, data='\n'.join(batch))
resp.raise_for_status()
print('Wrote', len(batch), 'rows')

Production tips:

Batch inserts to amortize HTTP overhead. Aim for 1k–50k rows per insert depending on row size.
Use persistent connections and retries with backoff.
Compress payloads (gzip) for large payloads; ClickHouse supports Content-Encoding.

Example: Kafka-backed ingest (recommended for large scale)

Write simulator events to Kafka topics (topic per experiment or folded by tenant). ClickHouse consumes via a Kafka table and a materialized view writes into MergeTree.

-- Create a Kafka table (ClickHouse server side)
CREATE TABLE kafka_quantum_raw (
  ts String,
  experiment_id String,
  run_id String,
  event_type String,
  qubit_index UInt16,
  shot_index UInt32,
  bitstring String,
  amplitude Float64,
  gate_name String,
  gate_duration Float32,
  error_rate Float32,
  metadata String
) ENGINE = Kafka('kafka:9092', 'quantum_events', 'clickhouse_group', 'JSONEachRow');

-- Materialized view to move data into MergeTree
CREATE MATERIALIZED VIEW mv_quantum_raw TO quantum.raw_events AS
SELECT
  parseDateTimeBestEffort(ts) AS ts,
  experiment_id,
  run_id,
  event_type,
  qubit_index,
  shot_index,
  bitstring,
  amplitude,
  gate_name,
  gate_duration,
  error_rate,
  metadata
FROM kafka_quantum_raw;

This decouples producers from ClickHouse and provides buffering and durability. Producers can be Python processes that push messages using confluent-kafka or aiokafka.

ETL: from raw events to actionable metrics

Raw events are verbose and expensive to query. Use materialized views to maintain aggregated summaries:

CREATE MATERIALIZED VIEW mv_hourly_metrics TO quantum.summary_metrics AS
SELECT
  toStartOfHour(ts) AS ts_hour,
  experiment_id,
  run_id,
  qubit_index,
  avgIf(error_rate, error_rate IS NOT NULL) AS avg_error_rate,
  quantileExact(0.5)(gate_duration) AS median_gate_duration,
  countIf(event_type = 'shot') AS shots,
  avgIf(amplitude, amplitude IS NOT NULL) AS fidelity
FROM quantum.raw_events
WHERE event_type IN ('shot', 'gate', 'metric')
GROUP BY ts_hour, experiment_id, run_id, qubit_index;

Benefits:

Near real-time visibility into qubit health by hour.
Small summary table makes dashboard queries fast.
Materialized views reduce ad-hoc aggregation cost; for infrastructure as code and deployment automation, consider pairing schema rollout with IaC templates so schema and ingestion pipelines are versioned together.

Managing retention and storage

Simulator raw data can grow fast. Use ClickHouse TTLs to drop or move raw rows after a retention window. For example, keep raw shots for 7 days and summaries for 365 days.

ALTER TABLE quantum.raw_events MODIFY TTL ts + INTERVAL 7 DAY;
ALTER TABLE quantum.summary_metrics MODIFY TTL ts_hour + INTERVAL 365 DAY;

Optionally, use move to cheaper storage with ClickHouse's TO DISK TTL to tier cold data.

Observability queries and dashboard design

Design dashboards around common workflows developers and researchers need:

Qubit Heatmap: per-qubit error rates across time windows.
Gate Duration Distribution: detect regressions in compilation/performance.
Shot Outcome Distribution: visualize top-k bitstrings and their probabilities.
Experiment Comparison: compare current run vs baseline for fidelity and error rates.

Example queries

Top-10 bitstrings for a run:

SELECT bitstring, count() AS cnt
FROM quantum.raw_events
WHERE experiment_id = 'exp-123' AND run_id = 'run-456' AND event_type = 'shot'
GROUP BY bitstring
ORDER BY cnt DESC
LIMIT 10;

Per-qubit hourly error rate trend:

SELECT ts_hour, qubit_index, avg_error_rate
FROM quantum.summary_metrics
WHERE experiment_id = 'exp-123'
ORDER BY ts_hour, qubit_index;

Gate duration percentiles (per gate):

SELECT gate_name, quantile(0.5)(gate_duration) AS p50, quantile(0.95)(gate_duration) AS p95
FROM quantum.raw_events
WHERE gate_name != ''
GROUP BY gate_name
ORDER BY p95 DESC;

Building dashboards

Grafana is an excellent choice in 2026: it has a mature ClickHouse datasource plugin and supports heatmaps, histograms, and table panels. Practical tips:

Precompute expensive aggregations with materialized views; dashboards should query summary tables.
Use templated variables for experiment_id and run_id so you can switch contexts quickly.
Use threshold annotations to surface SLA violations (error rate > X%) and link back to run metadata. For teams operating under formal SLA and auditing constraints, ensure alerting and retention policies are part of compliance reviews.

Advanced strategies and 2026 trends

As of 2026, several trends influence how teams build simulator observability:

GPU-accelerated simulation: cuQuantum and GPU-optimized simulators produce more throughput; you need Kafka or partitioned clickhouse pipelines to keep up.
Hybrid telemetry: combining statevector snapshots with sparse shot logs — store snapshots on object storage and index metadata in ClickHouse.
Edge/tenant multitenancy: ClickHouse's role-based access and per-tenant partitioning patterns help enforce data separation for multi-team labs.
Cost-aware retention: tighter TTLs and cold tiering reduce costs as experiment scale increases.

A practical architecture for 2026 quantum teams:

GPU-backed simulators stream high-throughput events to Kafka topics namespaced per tenant.
ClickHouse consumes Kafka, writes raw_events into MergeTree, and materialized views maintain hourly/daily summaries.
Large binary snapshots (statevectors) go to S3-compatible storage with a metadata pointer in ClickHouse; this fits into broader cloud-native architecture and storage tiering strategies.
Grafana dashboards query summary_metrics; alerts are triggered on deviations.

Operational considerations

Backpressure: use Kafka as the buffer; configure producer retries and delivery guarantees.
Schema evolution: include a metadata JSON to avoid frequent schema migrations; use ClickHouse's JSON functions to parse fields when needed.
Security: enforce TLS for HTTP and Kafka; use service accounts for ClickHouse writes — consider managed auth like authorization-as-a-service for short-lived credentials and role mapping.
Monitoring ClickHouse: monitor insert latencies, merge queues, and partition count to avoid performance problems.

Real-world example: from simulator job to dashboard

Walkthrough (condensed):

Spin up a Kafka cluster and ClickHouse (managed ClickHouse Cloud or self-hosted). Automate deployment using IaC templates for repeatable infrastructure.
Run a batch of 10,000 simulator jobs on a GPU cluster. Each job publishes per-shot JSON to topic quantum_events.
ClickHouse consumes the topic and materialized views populate summary_metrics. After the first hour, dashboards show per-qubit error rates and gate latency percentiles for live troubleshooting.
Researcher filters dashboards by experiment tag to compare noise models. Alert rules detect a qubit with rising error rate triggering re-calibration.

Checklist: what to implement first

Create raw_events and summary_metrics tables in ClickHouse.
Implement simple direct-insert pipeline for a single simulator to validate the schema.
Switch to Kafka for higher throughput and durability; if you’re building a resilient stack consider the broader cloud-native patterns.
Build materialized views for hourly/daily summaries and retention rules.
Hook Grafana to ClickHouse and build the top 4 panels (heatmap, gate distribution, top bitstrings, run comparison). For constrained budgets, reference low-cost observability patterns in guides like the low-cost tech stack playbooks to pick affordable components.

Actionable takeaways

Start with a compact raw schema that captures event_type, timestamps, and a metadata JSON to future-proof changes.
Batch inserts and use Kafka when simulator throughput grows — batching reduces cost and latency.
Materialized views are your friend: keep summary tables small and fast for dashboards. Consider coupling view creation with deployment pipelines using automation workflows so rollbacks and migrations are safe.
Retain raw shots short-term and summaries long-term to balance storage and query needs.
Integrate dashboards early so teams can iterate on what metrics matter (error_rate, fidelity, gate_latency).

Common pitfalls and how to avoid them

Ingest storm: unbatched per-shot writes will saturate ClickHouse — use batching or Kafka.
Unbounded cardinality: metadata with high-cardinality keys creates storage and query issues; normalize tags and index frequently-used keys.
Ignoring TTLs: raw simulator data grows quickly — plan retention early.
Lack of observability: monitor ClickHouse insert latency, merges, and Kafka lag to detect bottlenecks.

Conclusion & call to action

Streaming quantum simulator outputs into ClickHouse unlocks fast, scalable observability for experiment-driven development. Use a two-layer schema (raw + summary), choose Kafka when throughput demands it, and rely on materialized views and TTLs to keep queries fast and costs under control. In 2026, as ClickHouse adoption grows and simulation throughput increases thanks to GPU advancements, this pattern will let research and engineering teams iterate faster and ship more reliable quantum experiments.

Ready to implement this in your environment? Start with the schema and a single direct-insert producer. If you want a production-ready starter kit — including a Kafka producer template, ClickHouse table definitions, and Grafana dashboard JSON — download our open-source repo or contact us for a hands-on workshop to integrate ClickHouse with your simulator fleet. For reference architectures and deployment patterns, see reviews of affordable edge bundles and broader cloud-native guides above.

askqbit

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