Calibration Drift Detector

Compares calibration snapshots stored as vectors to catch drift.

Industry	Level	Difficulty	Recommended mode
Quantum computing	3 of 5	Advanced	flat

Coming soon

A complete, runnable Calibration Drift Detector reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Drift is noticed late.
• Calibration history is unused.
• Devices differ.

How SrvDB powers Calibration Drift Detector

1. Calibration parameters are stored as feature vectors per device.
2. New snapshots are compared with history.
3. Large distances flag drift.
4. One database per device.

What changes

Today	With Calibration Drift Detector on SrvDB
Late drift detection	Early flags
Unused history	Compared automatically
Device confusion	Per-device history

How it works

1. Vectorise calibration data (at least 128 values).
2. Store snapshots.
3. Compare new ones.
4. Flag drift.
5. Recalibrate.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• True cosine scores: scores in [-1, 1] mean the same in every mode, so thresholds are stable.
• Several databases: each database is a directory, so different data types or tenants stay separate.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Qubit frequency drift
• Gate fidelity changes
• Readout changes
• Noise profiles
• Device comparison
• Maintenance planning

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./calibration-drift-detector-db", dimension=128)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def similar_calibrations(snapshot, k=5):
    hits = db.search(embed(snapshot), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Feature design determines usefulness.

Caution

SrvDB is classical software: it stores ordinary vectors describing experiments and runs on regular computers, not on quantum hardware.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Quantum computing projects

Level	Project	What it does
1 · Beginner	Lab Notebook Search	Search the group’s notes, papers and run summaries.
2 · Intermediate	Lab Q&A Assistant	Answers questions from the group’s own knowledge with citations.
3 · Advanced	Calibration Drift Detector (this page)	Compares calibration snapshots stored as vectors to catch drift.
4 · Expert	Experiment Memory Agent	An agent remembering runs, plots and decisions across experiments.
5 · Frontier	Circuit Similarity Research	Research platform for circuit embeddings and similarity search.

See also: Use cases · Live examples (coming soon)