Autonomous Agents + ClickHouse: Building an OLAP-Powered Analyzer for Quantum Experiments

Hook: When noisy quantum hardware meets scattered data, you need speed and autonomy

Quantum teams in 2026 run hundreds of experiments a day: long calibration sweeps, randomized benchmarking batches, and cross‑platform comparisons. The pain is familiar — experiment artifacts are large, tooling is fragmented across cloud providers and SDKs, and manual triage of noisy runs slows research. What if an OLAP engine could surface anomalies in seconds and an autonomous agent could suggest targeted calibrations or schedule re‑runs — safely and auditable?

Executive summary — What you’ll get from this guide

This article shows how to combine ClickHouse (the high‑performance OLAP engine) with LLM‑driven autonomous agents to build an analyzer for quantum experiments. You’ll find:

• Architecture patterns for streaming and batch data pipelines into ClickHouse
• Schema and aggregation strategies tuned for quantum telemetry (shots, readout, calibration traces)
• Agent design that uses LLMs for interpretation but enforces safe permissioning and audit trails
• Cloud‑run and CI/CD examples to deploy the system reproducibly
• Operational best practices: access control, query limits, materialized views, and drift detection

Why ClickHouse + autonomous agents makes sense in 2026

ClickHouse’s rise through 2025 into 2026 — including significant late‑2025 funding and broad enterprise traction — reflects a growing need for sub‑second analytics on petabyte‑scale telemetry. ClickHouse’s columnar storage, MergeTree family, TTLs, and native integrations (Kafka engine, S3) are a natural fit for quantum experiment data where you need fast aggregations across dimensions like qubit, pulse sequence, and timestamp.

At the same time, autonomous agents have matured from desktop assistants into orchestrators for domain workflows. Products like Anthropic’s Cowork (early 2026) show agents increasingly operate on files and systems if given permission. For quantum labs, that means we can safely automate triage and calibration suggestions — but only with robust guards.

Core architecture — OLAP engine, ingestion, and the agent layer

High‑level components

• Ingest layer: Kafka or Pulumi jobs for streaming pulse‑level telemetry; batch Parquet or Avro from simulator runs
• Storage/OLAP: ClickHouse cluster for fast analytical queries, materialized views for pre‑aggregations, S3 for raw artifacts (waveforms, tomography results)
• Agent orchestration: A microservice that queries ClickHouse, runs LLM analysis (with retrieval), and emits safe recommendations
• Control plane: Access control, audit logs, and CI/CD to ensure reproducible deployments and safe rule changes

Data flow example

1. Quantum device or simulator streams records to Kafka topics per experiment.
2. ClickHouse Kafka engine ingests topics into staging tables, with a small consumer delay.
3. Materialized views compute rolling statistics per qubit (T1/T2 drift, readout fidelity, cross‑talk metrics).
4. An autonomous agent polls anomaly summaries and runs deeper queries on anomalous windows to propose calibration steps.
5. Recommendations are surfaced in a dashboard and, upon human approval, an orchestration job schedules calibrations.

Designing the ClickHouse schema for quantum telemetry

Design your schema for high cardinality and heavy aggregation. Use low‑level rows for raw shots and aggregated tables for analysis.

Example raw staging table (Kafka ingestion)

CREATE TABLE telemetry_raw (
    experiment_id String,
    run_id String,
    timestamp DateTime64(6),
    qubit UInt32,
    pulse_id String,
    shot UInt32,
    readout Float32,
    label String,
    metadata String
  ) ENGINE = Kafka SETTINGS
    kafka_broker_list = 'kafka:9092',
    kafka_topic_list = 'quantum.telemetry',
    kafka_group_name = 'ch_ingest_group',
    kafka_format = 'JSONEachRow';
  

Materialized view to commit into MergeTree

CREATE MATERIALIZED VIEW telemetry_mv TO telemetry_mt AS
  SELECT
    experiment_id,
    run_id,
    qubit,
    toStartOfInterval(timestamp, INTERVAL 1 minute) AS ts_min,
    avg(readout) AS mean_readout,
    stddevPop(readout) AS std_readout,
    count() AS shots
  FROM telemetry_raw
  GROUP BY experiment_id, run_id, qubit, ts_min;

  CREATE TABLE telemetry_mt (
    experiment_id String,
    run_id String,
    qubit UInt32,
    ts_min DateTime,
    mean_readout Float32,
    std_readout Float32,
    shots UInt64
  ) ENGINE = MergeTree()
    PARTITION BY toYYYYMM(ts_min)
    ORDER BY (experiment_id, qubit, ts_min)
    TTL ts_min + INTERVAL 90 DAY;
  

Key points: use minute buckets for rolling statistics, partition by month for manageable compactions, and TTLs to expire raw aggregation data you don’t need.

Patterns for real‑time anomaly detection

ClickHouse excels at fast group‑by analytics. Use it to compute baselines and monitor deviations using these patterns:

• Rolling window z‑score: compute mean and stddev over past N minutes and flag z>threshold.
• Quantile drift: compare recent quantiles against historical percentiles stored in a dedicated summary table.
• Change‑point detection: use simple difference of aggregates across adjacent windows; for complex detection, export features to a small model service.

Sample anomaly query (z‑score)

SELECT
    qubit,
    ts_min,
    mean_readout,
    (mean_readout - hist_mean)/hist_std AS z_score
  FROM (
    SELECT
      qubit,
      ts_min,
      mean_readout,
      anyLast(hist_mean) AS hist_mean,
      anyLast(hist_std) AS hist_std
    FROM telemetry_mt
    JOIN baseline_stats USING (qubit)
    WHERE ts_min >= now() - INTERVAL 10 minute
  )
  WHERE ABS(z_score) > 4
  ORDER BY ABS(z_score) DESC
  LIMIT 100;
  

These queries run quickly even on billions of rows because ClickHouse optimizes columnar scans and uses indexes on ORDER BY columns.

Autonomous agent design — safe, auditable, and effective

The agent's job: observe analytics outputs, interrogate ClickHouse for context, synthesize recommendations, and escalate or act under strict controls. This is a hybrid architecture: an LLM for explanation and suggestion, deterministic logic for actions and permission checks.

Principles for safe agents

• Least privilege: give agents read‑only ClickHouse credentials for analytics queries. Separate ingest and write credentials.
• Function whitelisting: the agent can call only predefined, audited functions (e.g., suggest_calibration, schedule_run_request) — never arbitrary SQL writes.
• Human‑in‑the‑loop (HITL): actions that change hardware or billing require explicit human approval.
• Audit trails: every agent decision, prompt, and downstream action gets logged to an append‑only table and external object storage for reproducibility — and tied into your incident playbooks and postmortems.
• Query and execution guards: set ClickHouse limits (max_execution_time, readonly settings) and monitor agent resource use.

Agent workflow (typical)

1. Polling rule detects anomalous aggregate (e.g., a qubit readout z>6 over last 5 minutes).
2. Agent runs a bounded set of deeper queries (last 24h traces, cross‑qubit correlations).
3. Agent formulates a concise observation and calls an LLM for natural‑language explanation and suggested calibrations.
4. Suggestion is validated against a rules engine; non‑risky suggestions are presented in a UI; risky ones go to HITL.
5. All steps are appended to agent_audit table and an immutable log bucket.

Example: Python pseudocode for the agent

from clickhouse_driver import Client
  import requests
  import time

  CH = Client('clickhouse-host', user='agent_readonly', password='***')
  LLM_API = 'https://api.example-llm.com/v1/generate'

  def find_anomalies():
      q = """
      -- z-score anomalies last 10 min
      SELECT qubit, ts_min, mean_readout, (mean_readout - hist_mean)/hist_std AS z
      FROM anomalies_view
      WHERE ts_min >= now() - INTERVAL 10 minute
      HAVING ABS(z) > 5
      ORDER BY ABS(z) DESC
      LIMIT 10
      """
      return CH.execute(q)

  def context_for(qubit, ts):
      q = f"SELECT * FROM telemetry_mt WHERE qubit={qubit} AND ts_min >= toDateTime('{ts - 3600}')"
      return CH.execute(q)

  def ask_llm(prompt):
      r = requests.post(LLM_API, json={'prompt': prompt, 'max_tokens': 512}, timeout=30)
      return r.json()['text']

  def main_loop():
      while True:
          anomalies = find_anomalies()
          for a in anomalies:
              qubit, ts, mean, z = a
              ctx = context_for(qubit, ts)
              prompt = build_prompt(qubit, ts, mean, z, ctx)
              explanation = ask_llm(prompt)
              log_decision(qubit, ts, explanation)
              notify_team(qubit, ts, explanation)
          time.sleep(60)
  

Note: the agent uses a read‑only ClickHouse account. Any write paths (suggested calibrations) go through a separate orchestrator that enforces approval and rate limits.

Safe permissions and ClickHouse controls

ClickHouse supports role management and settings you must leverage:

• RBAC: create roles like agent_readonly, ingest_writer, admin_audit.
• Query throttling: set max_execution_time, max_rows_to_read, and resource groups to avoid accidental full‑scan storms.
• Network controls: restrict ClickHouse to private subnets or VPC endpoints; terminate public access. Consider edge‑first or micro‑region patterns when you have distributed fleets.
• Row‑level considerations: if you have multi‑tenant experiments, implement dataset_id filters and grant access accordingly.

Retrieval and RAG patterns: giving the LLM usable context

LLMs are powerful for natural language but brittle with raw time‑series. Use ClickHouse to extract compact, high‑value context for the LLM:

• Top anomalous windows with precomputed statistics
• Representative waveforms or resampled summaries (downsample to a few hundred points)
• Metadata: SDK version, hardware firmware, environmental readings

Example retrieval pipeline

1. Agent queries ClickHouse for top N anomaly buckets.
2. For each bucket, run an aggregation that returns a compact JSON (histogram, quantiles, recent counts).
3. Feed that JSON into the LLM prompt as a structured context block.

Cloud‑run example: deploying the agent as a scalable service

Below is a stripped‑down Cloud Run deployment model (GCP) that runs the agent. Similar patterns apply to AWS Fargate or container apps.

Dockerfile (minimal)

FROM python:3.11-slim
  WORKDIR /app
  COPY requirements.txt ./
  RUN pip install -r requirements.txt
  COPY agent.py ./
  CMD ["python", "agent.py"]
  

Cloud Run considerations

• Use a dedicated service account with only the ClickHouse proxy and LLM API permissions you need — no broader GCP roles.
• Attach VPC Connector to reach ClickHouse in private VPC.
• Set concurrency and CPU/Memory to cap resource usage; enable request logs to BigQuery for auditing.

CI/CD for quantum workflows and agent code

Reproducibility is critical. Treat your data pipelines, agent prompts, and calibration playbooks as code. Example: GitHub Actions pipeline that runs unit tests, lints prompts, and deploys to Cloud Run.

GitHub Actions snippet

name: CI
  on: [push]
  jobs:
    test:
      runs-on: ubuntu-latest
      steps:
        - uses: actions/checkout@v4
        - name: Set up Python
          uses: actions/setup-python@v4
          with:
            python-version: 3.11
        - name: Install deps
          run: pip install -r requirements.txt
        - name: Run tests
          run: pytest -q
    deploy:
      needs: test
      runs-on: ubuntu-latest
      if: github.ref == 'refs/heads/main'
      steps:
        - uses: actions/checkout@v4
        - name: Configure gcloud
          uses: google-github-actions/auth@v1
          with:
            workload_identity_provider: ${{ secrets.WORKLOAD_IDENTITY_PROVIDER }}
            service_account: ${{ secrets.SERVICE_ACCOUNT }}
        - name: Deploy to Cloud Run
          run: |
            gcloud run deploy q-agent --image gcr.io/$PROJECT_ID/q-agent:latest --region us-central1 --platform managed
  

Also version your ClickHouse migrations and materialized view changes. Use migration tools to apply schema updates and track them in Git.

Operational playbook: how to respond to agent recommendations

Define SLAs and escalation policies. A sample playbook:

• Severity 1 (hardware fault): Agent flags recurring hard failures → alert on‑call, quarantine device, open ticket
• Severity 2 (calibration drift): Suggest recalibration → schedule automated calibration with HITL approval
• Severity 3 (minor noise): Recommend param tweak (e.g., pulse amplitude) → apply automatically if below threshold of change

Best practice: never let an autonomous agent execute irreversible hardware actions without an approval signature and audit record.

Examples and case studies (realistic scenarios)

Case 1: Fast spike detection saves a multi‑day run

A research group running tomography on 20 qubits saw a subtle readout drift on qubit 7 that would have invalidated a 48‑hour sweep. The agent detected a z‑score of 7 in the 15‑minute window, pulled 12 hours of context, and suggested a readout amplifier retune. Human approval started a 10‑minute calibration; the run resumed and saved the experiment.

Case 2: Cross‑device pattern suggests firmware regression

Aggregating telemetry across devices in ClickHouse revealed identical patterns of increased thermal noise across the fleet after a firmware update. The agent recommended rollback and flagged correlated environmental sensors. The engineering team used ClickHouse queries to identify affected batches and rolled back in a controlled window.

Future trends and predictions (2026 and beyond)

• ClickHouse and other OLAP systems will become the default observability store for quantum stacks because of their cost‑performance for high cardinality telemetry.
• Autonomous agents will shift from recommending to orchestrating more closed‑loop calibration — but only where robust guardrails exist.
• Hybrid models — small local models for immediate heuristic checks and cloud LLMs for explanation — will reduce latency and cost while preserving interpretability. Expect more work on on‑device checks and edge personalization to reduce round trips.
• Standardized experiment metadata schemas (2026) will improve RAG prompt quality and enable cross‑institution reproducibility.

Checklist — What to build first

1. Stream a representative telemetry feed into ClickHouse (Kafka engine or bulk Parquet)
2. Create minute/second materialized views for per‑qubit aggregates
3. Deploy a read‑only agent service that polls anomaly queries and logs decisions
4. Implement RBAC roles and query limits for the agent account
5. Wire a human approval flow for any action that affects hardware

Actionable takeaways

• Leverage ClickHouse for sub‑second aggregation across large quantum telemetry — materialized views and MergeTree engines are your friends.
• Design agents with strict separation of concerns: LLMs for explanation, deterministic logic for actions, and an orchestrator for enforcement.
• Guard every step: least privilege, audit trails, and human approval for irreversible operations.
• Automate safely: auto‑apply low‑risk calibration tweaks and reserve human approval for anything that could damage hardware or cost money.
• CI/CD everything: prompts, SQL migrations, and deployment scripts should be versioned and testable.

Closing — why this matters now

In 2026, quantum teams operate at scale and need analytics that match their experiment throughput. ClickHouse gives the speed; agents give the automation — but only when combined with careful access controls and reproducible pipelines. Adopting this pattern accelerates research, reduces wasted runs, and turns noisy telemetry into actionable intelligence.

Call to action

Ready to prototype an OLAP‑driven autonomous analyzer? Start with a 2‑week sprint: ingest telemetry into a ClickHouse sandbox, deploy a read‑only agent on Cloud Run or edge nodes, and run the anomaly checklist above. If you want a starter template (ClickHouse schema, agent code, Cloud Run and GitHub Actions setups), download our open‑source scaffold and join the qbitshare community to share reproducible experiments and prompts.

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