Circuit Similarity Research

Research platform for circuit embeddings and similarity search.

Industry	Level	Difficulty	Recommended mode
Quantum computing	5 of 5	Frontier	flat

Coming soon

A complete, runnable Circuit Similarity Research reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Equivalent circuits are hard to spot.
• Circuit libraries grow.
• Embedding methods are experimental.

How SrvDB powers Circuit Similarity Research

1. Circuit embeddings (from research models) are stored in SrvDB.
2. Exact flat search keeps experiments reproducible.
3. Rust API for pipelines.
4. Results archived with versioned formats.

What changes

Today	With Circuit Similarity Research on SrvDB
Manual comparison	Similarity search
Ad-hoc scripts	Reusable store
Irreproducible searches	Exact search

How it works

1. Choose an embedding method.
2. Embed circuits.
3. Search.
4. Evaluate.
5. Publish.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• Rust API: embed SrvDB directly in Rust services and devices.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• search_batch: runs many queries at once, in parallel inside Rust.

Use cases

• Equivalent-circuit candidates
• Error-syndrome pattern retrieval
• Quantum chemistry result lookup
• QML experiment store
• Benchmark archives
• Teaching sets

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("./circuit-similarity-research-db", dimension=256)

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

Limits and honest notes

• Research direction; results depend on the embedding method.

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	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 (this page)	Research platform for circuit embeddings and similarity search.

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