qdsv-bridge 0.1.1

Lightweight Python client for QDSV Bridge, a controlled semantic-to-circuit compiler with use/build/expert modes.

QDSV Bridge Developer Preview

Lightweight Python SDK for QDSV Bridge, a controlled semantic compiler built on QDSV (Quantum Declarative Semantic Value) that turns supported problem-family specifications into problem-derived circuit materializations, QDSV IR, oracle specs, QASM/Qiskit artifacts, or expert construction inputs.

QDSV Bridge is not a template selector and not a free arbitrary circuit generator. It is a restricted semantic-to-circuit bridge for supported problem families. Its core rule is simple: do not force the problem into a prefabricated circuit; derive the circuit or construction inputs from the semantic problem specification.

problem family spec
-> family ontology validation
-> semantic ProblemSpec / IR
-> oracle specification
-> materialization policy
-> generated circuit artifact or expert construction inputs

The SDK is a client only. It does not include the private QDSV Runtime, CAP, backend selector, lowering internals, QuEST/Aer/IBM adapters, or advanced orchestration.

pip install qdsv-bridge

Why This Exists

Traditional quantum workflows often start by asking users to choose a circuit, encoding, ansatz, or measurement pattern. That can force the data to adapt to a circuit.

QDSV Bridge starts from a controlled semantic family. The family defines what kind of problem is allowed, what concepts belong to it, what patterns are excluded, and what evidence must be produced. For users who need circuit ecosystems, Bridge derives a new circuit artifact from that semantic specification instead of asking the user to adapt the problem to a ready-made template. For expert constructors, Bridge can also return the key semantic inputs needed to design a custom circuit without forcing a final circuit.

For example, in signal classification:

prepared signals
-> semantic_signal_classification
-> preserve signal geometry
-> oracle / IR / QASM blueprint

This is designed to avoid a fixed pattern such as:

forced reduction -> angle encoding -> fixed ansatz -> fixed measurement

Bridge Modes

QDSV Bridge is one SDK with multiple output depths. The modes do not change the core principle: the circuit, when delivered, is derived from the semantic problem specification.

Mode	Intended user	What Bridge returns	Message
use	Basic user who wants to solve without designing circuits	A new problem-derived circuit artifact, simple explanation, information-loss risk, usage recommendation and ready-to-run example	No need to design the circuit; Bridge generates it from your problem.
build	Intermediate user who understands Qiskit, QASM or quantum workflows	A new problem-derived circuit artifact plus QASM/Qiskit, oracle spec, IR summary, preservation report, estimated qubits/depth and digests	Take this circuit generated from semantics and adjust or integrate it into your stack.
expert_prepare	Expert constructor who wants to design a custom circuit	Validated family, ProblemSpec/IR, oracle spec, predicates, target state/goal, constraints, relevant variables, information-loss risk, encoding/measurement suggestions, estimated limits and evidence. It does not force a final circuit.	We give you the right inputs to design a circuit faithful to the problem.
expert_evaluate	Expert evaluator who wants to compare QDSV materializations	Suggested QDSV circuit artifact, materialization variants, comparison and preservation report	Compare QDSV materializations and decide which one preserves the problem best.

Python helpers:

client.generate(spec)  # mode="use"
client.build(spec)     # mode="build"
client.prepare(spec)   # mode="expert_prepare"
client.evaluate(spec)  # mode="expert_evaluate"

Or pass the mode explicitly:

client.export(spec, mode="build")
client.explain(spec, mode="expert_prepare")

CLI:

qdsv-bridge export spec.json --mode use
qdsv-bridge export spec.json --mode build
qdsv-bridge export spec.json --mode expert_prepare
qdsv-bridge export spec.json --mode expert_evaluate

Quick Start

from qdsv_bridge import QDSVBridgeClient

client = QDSVBridgeClient()

spec = {
    "family": "semantic_signal_classification",
    "state_space": {
        "kind": "finite_candidates",
        "candidate_count": 300,
        "candidate_id": "eeg_window",
    },
    "signals": [
        "dwt_cD3_std_score",
        "std_score",
        "activity_score",
        "energy_score",
        "dwt_cD2_std_score",
        "complexity_score",
    ],
    "goal": {
        "kind": "binary_marking",
        "positive_state": "ictal",
    },
    "materialization_policy": {
        "preserve_signal_geometry": True,
        "avoid_fixed_ansatz": True,
        "avoid_forced_dimensionality_reduction": True,
        "report_information_loss": True,
    },
    "target": {
        "format": "qasm3",
        "backend_family": "qiskit",
    },
    "limits": {
        "max_qubits": 10,
        "max_depth": 300,
    },
}

result = client.build(spec)

print(result["status"])
print(result["bridge_mode"])
print(result["circuit"])
print(result["semantic_preservation_report"])
print(result["artifact"]["content"])

By default, QDSVBridgeClient() points to the public cloud API:

https://api.qdsv.cloud/api

For the local/private Docker demo, use:

client = QDSVBridgeClient.local()

Public Endpoints

The SDK calls the QDSV API:

• GET /api/bridge/families
• POST /api/bridge/validate
• POST /api/bridge/compile
• POST /api/bridge/explain
• POST /api/bridge/export

Default cloud endpoint:

client = QDSVBridgeClient(api_url="https://api.qdsv.cloud/api")

Local/private Docker endpoint:

client = QDSVBridgeClient.local()

Supported Families

Developer Preview families:

• semantic_signal_classification
• predicate_marking
• state_similarity
• combinatorial_relation
• distribution_sampling

Each family has:

• ontology boundary;
• allowed state-space kinds;
• allowed goal kinds;
• allowed semantic operations;
• excluded patterns;
• public limits;
• export targets;
• evidence / digest contract.

What It Exports

Depending on target.format, QDSV Bridge can export:

• problem_spec
• ir
• oracle_spec
• qasm2
• qasm3
• qiskit_blueprint

For use and build, circuit-oriented targets return a circuit artifact derived from the semantic spec, with explicit QDSV oracle/digest information and preservation metadata. In Developer Preview, some targets are returned as integration blueprints with semantic oracle insertion points so external platforms can inspect, execute, optimize or complete the materialization safely.

For expert_prepare, Bridge may intentionally return construction inputs instead of forcing a final circuit. This is useful when an expert wants to design a custom circuit with the correct semantic ingredients.

Important Boundaries

QDSV Bridge does not expose:

• QDSV Runtime internals;
• CAP internals;
• backend selection heuristics;
• private QuEST/Aer/IBM adapters;
• native lowering internals;
• unrestricted circuit generation;
• arbitrary Python execution;
• prefabricated circuit selection as the main product;
• user-supplied free circuits as family contracts.

The product principle is:

controlled semantic family
-> problem-derived circuit materialization or expert inputs
-> auditable export

Not:

any input
-> arbitrary circuit

Open SDK, Private Runtime

This repository is open-core:

• Open under MIT: Python client SDK, examples, docs and tests.
• Not included: QDSV Runtime, ontology compiler internals, CAP, lowering, advanced materialization, backend adapters, private endpoints, secrets or production configuration.

QDSV, QIntent and Qruba names and marks are project marks of their respective owners. The MIT License for this repository does not grant trademark rights.