From ClickHouse to Qubits: Designing Observability for Quantum Data Pipelines

Hook: Why observability is the blocker between your quantum experiments and reproducible results

Quantum engineers and platform teams in 2026 face a familiar, painful truth: running experiments is only the start. The steepest cost comes from not seeing why an experiment failed, why a qubit suddenly degraded, or why hybrid workflows that touch a cloud QPU and on-prem simulator behave nondeterministically. Traditional observability systems for classical services won’t map directly to quantum telemetry — but their principles do. This article shows how to borrow large-scale observability practices (including ClickHouse-backed analytics) to build reliable telemetry, analytics, and alerting for quantum labs and hybrid deployments.

Executive summary — the inverted pyramid up front

Most important takeaways:

• Split concerns: real-time metrics and alerting (Prometheus/Push) for immediate SRE needs; long-term experiment/searchable analytics (ClickHouse) for research, compliance and drift analysis.
• Tube your data: use Kafka or a cloud streaming layer to decouple instrumentation from storage and enable high-throughput ingestion of shots, pulses, logs and calibration events.
• Design schemas for time-series, events and traces: MergeTree/ReplicatedMergeTree for experiment records; materialized views for rollups; TTL for storage control.
• Alert smart: combine deterministic thresholds with statistical anomaly detection and SLO-driven alerting to reduce noise from quantum hardware variability.
• Operationalize: automations, runbooks, and an observability maturity model tailored to quantum labs.

Context in 2026: why ClickHouse and classical observability patterns matter now

Large-scale analytics databases like ClickHouse became mainstream for event-driven analytics across fintech, adtech and cloud ops. In late 2025 ClickHouse received a major funding round and adoption continued to accelerate — a sign that teams need high-throughput, low-latency analytical stores to query billions of events cheaply. That capability maps perfectly to quantum telemetry, where each experiment can emit thousands to millions of shots, pulses, and diagnostic samples.

ClickHouse's adoption surge in 2025–2026 shows the market demand for fast OLAP systems that can store and query high-cardinality event data at scale — ideal for quantum experiment analytics.

Quantum observability: the data types you must capture

Before designing pipelines, list the data you need. Minimal set for a production quantum lab:

• Experiment metadata: experiment_id, user, SDK (Qiskit/Cirq/PennyLane), job_id, target_backend, timestamp, versioned circuits.
• Shots and outcomes: per-shot bitstrings, counts, measurement timestamps, per-shot latency.
• Pulse-level telemetry: waveform summaries, sample-level anomalies, DAC/ADC traces (when available).
• Calibration events: T1/T2/Ramsey/Readout calibrations and results, gate fidelities.
• Hardware state: qubit temperature, drift metrics, cryostat events, RF chain logs.
• Scheduling & queue: queue wait times, cancellations, preemption events.
• Traces & logs: RPC latency between controller and QPU, SDK errors, compiler warnings.

Reference architecture: hybrid observability stack

Combine tried-and-tested components:

1. Instrumentation libraries in SDKs (Qiskit/Cirq hooks) that emit structured events.
2. Streaming ingestion (Kafka/Kinesis) to buffer & decouple producers from storage.
3. Real-time metrics: Prometheus + Pushgateway for SRE alerts and dashboards.
4. Distributed tracing: OpenTelemetry to trace hybrid runs that span cloud QPUs and local services.
5. Long-term analytics and ad-hoc queries: ClickHouse for high-cardinality experiment/event data.
6. Log store for verbose debug: Loki/Elastic for unstructured logs with links to experiment IDs.

Why ClickHouse?

ClickHouse excels at event analytics: very high ingestion rates, adaptive compression, and fast OLAP queries over billions of rows. For quantum telemetry it offers:

• Efficient storage of per-shot outcomes across many experiments.
• Fast, ad-hoc analytics for drift detection and cohort analysis.
• Materialized views and TTLs for retention and efficient rollups.

Practical pipeline design

Below is a practical pipeline you can implement this quarter. I’ve used patterns that scale from a single lab to hybrid cloud QPU fleets.

1) Instrumentation: annotate everything

Embed structured telemetry in your SDK wrappers. Always include experiment_id, run_id, timestamp, and a standard schema_version. Example fields for an SDK event:

{
  "experiment_id": "exp-2026-01-07-42",
  "run_id": "run-0001",
  "sdk": "qiskit",
  "backend": "ibm_qpu_7",
  "shot_index": 234,
  "outcome": "101",
  "latency_ms": 123.4,
  "schema_version": 1
}

Write small wrappers or a plugin for Qiskit/Cirq/PennyLane that emits these JSON events to Kafka or directly to a collector over gRPC/HTTP. For teams debating on-prem vs cloud ingestion, see the practical decision matrix on database migrations for analogous trade-offs when moving heavy write workloads.

2) Streaming: Kafka as the nerve cord

Use Kafka to decouple producers (SDKs, controllers) from consumers (ClickHouse ingestion, Prometheus exporters). Benefits:

• Backpressure handling for bursts of high-shot experiments.
• Replaying historical streams for debugging or reprocessing.
• Fan-out to different consumers (analytics, model training, archives).

Design Kafka topics and retention with edge auditability and replayability in mind so you can reproduce runs and meet compliance needs.

3) Ingest into ClickHouse — schema patterns and examples

Design your ClickHouse tables around common query patterns: per-qubit trends, per-experiment aggregates, and joinable calibration records.

Example table for per-shot results (high-cardinality):

CREATE TABLE quantum_shots
(
  experiment_id String,
  run_id String,
  shot_index UInt32,
  outcome String,
  sdk String,
  backend String,
  timestamp DateTime64(3),
  latency_ms Float32,
  tags Nested (key String, value String)
)
ENGINE = ReplicatedMergeTree('/clickhouse/tables/{shard}/quantum_shots','{replica}')
PARTITION BY toYYYYMM(timestamp)
ORDER BY (backend, experiment_id, timestamp)
TTL timestamp + INTERVAL 90 DAY
SETTINGS index_granularity = 8192;

Design a calibration table for sparser writes but heavy joins:

CREATE TABLE calibrations
(
  calib_id String,
  backend String,
  qubit_id UInt16,
  metric_name String,
  metric_value Float32,
  recorded_at DateTime64(3),
  details String
)
ENGINE = ReplacingMergeTree(recorded_at)
PARTITION BY toYYYYMM(recorded_at)
ORDER BY (backend, qubit_id, recorded_at);

Use Materialized Views for fast aggregate queries. Example: daily per-qubit fidelity rollup.

CREATE MATERIALIZED VIEW mv_daily_qubit_fidelity
ENGINE = SummingMergeTree
PARTITION BY toYYYYMM(day)
ORDER BY (backend, qubit_id, day)
AS
SELECT
  backend,
  qubit_id,
  toDate(timestamp) AS day,
  count() AS experiments,
  avg(metric_value) AS avg_fidelity
FROM calibrations
WHERE metric_name = 'two_qubit_fidelity'
GROUP BY backend, qubit_id, day;

4) Real-time monitoring and alerting (Prometheus + Alertmanager)

Prometheus handles short-term SRE signals: queue lengths, controller CPU, RPC latency, experiment failure rates. Exporters feed Prometheus with summarized metrics (not every shot!). For example, your SDK can emit per-run counters (shots_total, shots_failed) to a Prometheus Pushgateway once a run finishes. For best practice on developer-facing observability and tooling, see notes on edge-first developer experience.

Example Prometheus alert rule (YAML):

groups:
- name: quantum-slo.rules
  rules:
  - alert: QubitFidelityDrop
    expr: avg_over_time(qubit_two_fidelity{backend="ibm_qpu_7"}[1h]) < 0.90
    for: 10m
    labels:
      severity: critical
    annotations:
      summary: "Qubit fidelity below 90% for backend ibm_qpu_7"
      runbook: "https://intranet/runbooks/qubit_fidelity_drop"

5) Hybrid tracing: OpenTelemetry for distributed runs

Hybrid workflows commonly span a local orchestration service, a cloud compiler, and a remote QPU. Use OpenTelemetry traces to track latency across these components and to automatically attach experiment_id and run_id tags. Techniques used to tune client runtimes and bundlers for mobile and edge apps (for example, Hermes/Metro tweaks) also apply when you optimize trace sampling and exporter throughput—see guidance on runtime tuning at scale.

Analytics & reliability patterns you should copy from large-scale ops

Below are mature patterns that translate well to quantum labs.

High-cardinality indexing and partitioning

Quantum telemetry is high-cardinality by nature (many qubits, many runs, many users). Choose ORDER BY keys that match query patterns (e.g., backend + experiment_id + timestamp) and partition by time to efficiently drop old data with TTLs.

Materialized rollups for day-to-day dashboards

Don’t query raw shots for SRE metrics. Create rollups (minute/hour/day) for latency, error rates, and per-qubit fidelity. Materialized views with SummingMergeTree or AggregatingMergeTree give fast dashboard response and reduce load. For teams struggling with tool proliferation, a tool-sprawl audit helps decide which rollups and tooling to keep.

Anomaly detection: blend deterministic rules with statistics

A single qubit fidelity drop shouldn’t trigger a pager. Use both:

• Threshold alerts for catastrophic conditions (cryostat failure, connectivity loss).
• Statistical alerts using z-scores or moving median and quantiles for drift detection.

Example SQL to compute a rolling z-score for a qubit fidelity in ClickHouse:

SELECT
  backend,
  qubit_id,
  day,
  avg_fidelity,
  (avg_fidelity - avg_over_window(avg_fidelity, 7)) / stddevPopOverWindow(avg_fidelity, 7) AS z_score
FROM mv_daily_qubit_fidelity
WINDOW w AS (PARTITION BY backend, qubit_id ORDER BY day ROWS BETWEEN 6 PRECEDING AND CURRENT ROW)
WHERE day >= today() - 30;

Noise-aware alerting

Quantum hardware is noisy and nonstationary. Implement:

• Adaptive thresholds that account for baseline drift.
• Alert suppression windows following maintenance or calibration runs.
• Composite alerts that require multiple signals (fidelity drop + increased failure rate + cryostat alarm) before paging.

Operational playbooks and runbooks

Every alert needs a runbook. Examples of playbooks you should create:

• Qubit drift: check calibrations, run calibration suite, compare to 7-day baseline.
• High experiment failure rate: inspect queue times and SDK error logs; replicate minimal failing job locally.
• Pulse anomaly: pull waveform trace from raw store, run signal-processing scripts to find clipping or saturations.

Security, governance and experiment lineage

Telemetry may include sensitive experiment metadata. Practices to adopt:

• Access control on ClickHouse and Kafka topics — RBAC for research vs SRE queries. Consider cloud teams' changes required by EU data residency rules.
• Immutable experiment IDs and signed metadata for reproducibility.
• Audit logs to track who ran what experiment and when.

Cost, scaling and performance tips

ClickHouse reduces storage cost with compression and efficient encodings, but you still need to plan:

• Partition by month and TTL raw shots after 90 days while keeping aggregates forever.
• Use sampling queries for ad-hoc debugging of very large experiments.
• Shard by backend or lab to distribute load across ClickHouse clusters. For cache and cost-aware strategies, consider carbon-aware caching and edge caching patterns.

Case study: a mid-size lab’s migration to ClickHouse analytics (illustrative)

Scenario: a university lab with 6 local QPUs and access to two cloud QPUs experienced long mean-time-to-diagnosis for failed experiments. They implemented:

1. SDK-level instrumentation that emitted structured events to Kafka.
2. ClickHouse cluster with per-shot and per-calibration tables and materialized views for per-qubit daily rollups.
3. Prometheus for real-time SRE alerts and Grafana dashboards linked to ClickHouse for detailed drilldowns.

Results within 3 months:

• Time-to-diagnosis dropped from >6 hours to <45 minutes.
• False-positive alerts decreased by 70% after implementing composite and statistical alerts.
• Researchers could reproduce failing runs using replayed Kafka streams and raw clickhouse data.

Advanced strategies (2026+): ML-driven observability & causal inference

As 2026 progresses, teams are applying ML to predict qubit failure and to surface causal signals. Practical steps:

• Train models on ClickHouse features (rolling fidelities, cryostat logs, queue metrics).
• Use uplift/causal inference techniques to test if a calibration reduced error in production.
• Deploy lightweight models for realtime scoring and feed predictions back into Prometheus as metrics for alerting. See work on agentic AI and quantum agents for intersections between predictive models and quantum control.

Checklist: implementation milestones (30/60/90 days)

1. 30 days: Instrumentation hooks in SDKs; Kafka topic; proof-of-concept ClickHouse table; Prometheus exporter for core metrics.
2. 60 days: Materialized views for common KPIs; Grafana dashboards; simple alert rules and runbooks.
3. 90 days: Full retention policies; anomaly detection pipelines; runbook automations (e.g., auto-trigger calibrations); model training pipeline connected to ClickHouse features.

Common pitfalls and how to avoid them

• Storing raw shots forever — use TTL and rollups. You can apply content lifecycle patterns from edge caching and appliance reviews such as the ByteCache field review to decide retention on the edge.
• Alert fatigue — combine signals and use statistical baselines.
• High-cardinality indexes without partitioning — plan ORDER BY and partitions around query patterns.
• Missing metadata — always attach experiment_id, SDK version, and backend config.

Actionable templates and snippets (ready to reuse)

Copy-pasteable starter templates:

ClickHouse TTL policy for shots

ALTER TABLE quantum_shots MODIFY TTL timestamp + INTERVAL 90 DAY;

Materialized view for hourly shot error rates

CREATE MATERIALIZED VIEW mv_hourly_errors
ENGINE = SummingMergeTree
PARTITION BY toYYYYMM(hour)
ORDER BY (backend, hour)
AS
SELECT
  backend,
  toStartOfHour(timestamp) AS hour,
  sum(if(outcome = 'error', 1, 0)) AS errors,
  count() AS shots
FROM quantum_shots
GROUP BY backend, hour;

Final thoughts and future predictions for 2026–2028

In 2026 we see two converging trends: enterprise-grade analytics systems (ClickHouse et al.) becoming default for high-throughput event data, and quantum teams demanding observability that treats experiments as first-class telemetry citizens. Over the next 24 months expect tighter integrations between quantum SDKs and observability primitives, standard telemetry schemas across cloud providers, and ML-driven predictive maintenance that reduces experiment flakiness.

Adopting classical observability patterns now is a force-multiplier. You gain faster debugging, reproducibility, and the ability to scale a quantum program from a single lab to hybrid cloud deployments without losing operational control.

Call to action

Start practical: instrument one SDK wrapper, stream a week of experiments into Kafka, and load them into a ClickHouse test cluster. If you want a jumpstart, download our starter ClickHouse schema, Prometheus rules, and Grafana dashboards for quantum telemetry from our GitHub repo — or contact the askQBit team for a hands-on workshop tailored to your lab.

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