QDSV Bridge
Source/package version: 0.6.2. See the PyPI badge for publication status.
From Business Meaning To Quantum Artifacts
QDSV Bridge transforms supported business rules, prepared data and functional objectives into semantically validated and optimized logical quantum artifacts. It is QDSV's interoperability layer for today's circuit-based quantum ecosystem: the semantic problem remains primary, and logical circuits are derived when the destination requires a circuit artifact.
For supported problem families, users do not need to design gates, quantum registers, ancillas, reversible cleanup or a framework-specific quantum model. The user describes what the problem means; Bridge derives and verifies its quantum realization.
The circuit is a delivery format, not the source of truth.
The semantic problem specification remains canonical while Bridge derives, validates and documents:
- the supported reversible construction, registers, controls and cleanup;
- an immutable canonical logical circuit;
- an optional exact logical optimization linked to its parent by digest;
- semantic-equivalence and no-regression validation;
- inspectable OpenQASM/Qiskit artifacts, resources and construction evidence;
- a traceable handoff for downstream simulator or hardware execution.
The user provides bounded candidates, prepared numeric inputs, functional rules, thresholds, priorities and objectives. Bridge derives the supported quantum construction without requiring the user to design it. It never fabricates an unsupported construction or embeds precomputed answers when a request cannot be materialized.
Business-First Quickstart
Start with the candidates and the rule they must satisfy. This example asks
Bridge to mark suppliers whose quality is at least 700 and whose compliance
flag is 1:
from qdsv_bridge import (
QDSVBridgeClient,
build_predicate_spec,
select_recommended_artifact,
)
suppliers = [
{"candidate_index": 0, "supplier_id": 101, "quality": 820, "compliance": 1},
{"candidate_index": 1, "supplier_id": 102, "quality": 680, "compliance": 1},
{"candidate_index": 2, "supplier_id": 103, "quality": 760, "compliance": 0},
]
business_rule = {
"op": "and",
"args": [
{
"op": "gte",
"left": {"op": "field", "name": "quality"},
"right": {"op": "const", "value": 700},
},
{
"op": "eq",
"left": {"op": "field", "name": "compliance"},
"right": {"op": "const", "value": 1},
},
],
}
spec = build_predicate_spec(rows=suppliers, predicate=business_rule)
result = QDSVBridgeClient().generate(spec)
recommended = select_recommended_artifact(result)
print(result["status"])
print(result["recommended_artifact_role"])
print(recommended["format"])
print(result["construction_verification"])
The user does not provide gates, registers, ancillas, reversible cleanup or
expected answers. candidate_index is the stable circuit-domain identity;
supplier_id remains the organization's business reference.
build_predicate_spec() preserves both, normalizes the declared rule without
evaluating it, and applies portable defaults for the logical artifact. Advanced
users can override those defaults through its typed parameters.
The successful response can contain:
- an immutable canonical logical artifact;
- an accepted optimized child artifact when exact validation and no-regression checks pass;
- the recommended artifact role and inspectable OpenQASM/Qiskit content;
- construction verification, resource metrics, digests and traceability;
- a handoff contract for downstream simulation or hardware execution.
The complete runnable version is
examples/compound_business_predicate.py.
How Bridge Differs From Quantum Synthesis Platforms
Most quantum-development platforms reduce the work required to construct a circuit but still expect the user to define or review a quantum program. Bridge starts one layer earlier: with the bounded problem and its functional meaning.
The distinction is not merely fewer lines of code. It is a different user responsibility:
| Dimension | Classiq | QDSV Bridge |
|---|---|---|
| Starting point | An explicit Qmod quantum model, written manually or with AI assistance | Prepared business data and a supported semantic rule |
| User thinks about | Quantum functions, variables, model behavior and synthesis | Candidates, values, criteria, thresholds, priorities and outcomes |
| Canonical source | The explicit quantum model | The semantic problem specification |
| Quantum-specific user work | Define or review the quantum model | No quantum program design for supported problem families |
| Reversible realization | Synthesized from the Qmod model | Derived from the semantic rule and linked to semantic digests |
| Optimization | Broad synthesis, including hardware-aware options | Exact target-independent logical optimization with replay and no-regression acceptance |
| Execution | Integrated simulator and provider workflows | Deliberately separated through Qiskit, Qruba or QDSV Runtime/HSP |
| Primary strength | Broad quantum-engineering and execution platform | Higher problem-level abstraction and less explicit quantum engineering |
Classiq provides a high-level environment for designing and synthesizing quantum programs. For supported problem families, QDSV Bridge removes quantum-program design from the end-user workflow.
Classiq provides a broader integrated quantum-development environment. Bridge provides an earlier entry point for organizations that want to begin with the problem rather than with a quantum program. The comparison above describes the public workflows and architectural responsibility boundary; it is not a claim of universal circuit-performance superiority. See the Classiq documentation for its current public workflow.
Installation
Install the client:
pip install qdsv-bridge
Install the optional Qiskit inspection dependencies:
pip install "qdsv-bridge[qiskit]"
The Qiskit extra is capped at qiskit>=2,<3 to preserve compatibility with the
currently tested Qiskit major version. The Public Preview does not require an
API key.
Current Availability And Operational Boundaries
QDSV Bridge 0.6.2 is publicly available through PyPI and the Qiskit Ecosystem
for bounded real-world validation. Its supported capability catalog and
deployment options continue to expand under the Public Preview contract.
- Bridge supports bounded problem families and explicit resource limits; it does not accept every arbitrary business or quantum program.
- The public service is provided without an SLA and may change or be
temporarily unavailable before
1.0. - Bridge does not execute on a simulator or QPU, select a provider, manage credentials, route to hardware, mitigate noise or interpret experiments.
- Do not send confidential, regulated or secret data to the public service.
Bridge validates the semantic-to-circuit construction path and reports the resources required by the generated artifact. It can derive an exact, target-independent logical optimization and recommend it only when contractual replay passes and protected logical metrics do not regress. The canonical artifact is never replaced silently.
The public SDK supports Python 3.9 and later. Before 1.0, minor releases may
introduce contract changes; deprecations and migration notes are recorded in
the changelog.
Technical Workflows
Use build_predicate_spec() for explicit public predicates with nested boolean
composition and field-to-field comparisons. Use the lower-level specification
contract when you need to freeze artifact format, backend family, evidence or
resource limits. In either path, do not include labels, expected decisions or
precomputed predicate results in the input rows.
When materialization succeeds, generate() returns the canonical circuit and
loading guidance. Bridge attempts the public qiskit_structural_exact_v1
logical optimization by default. select_recommended_artifact() returns the
accepted optimized artifact when available and otherwise returns the canonical
artifact.
The optimization is exact and target-independent. It does not perform layout, routing, scheduling, calibration-aware selection, noise suppression, mitigation or hardware execution. Those operations remain downstream in Qiskit, Qruba or QDSV Runtime/HSP.
For a minimal multi-criteria ScoreModel example, run
examples/score_model_v2.py. Prepared metrics and
the cutoff use one declared scale, and priority represents a domain priority,
not the position of a criterion. The SDK example does not reproduce private
ScoreModel aggregation or compiler rules.
Technical Reference
The detailed contracts remain versioned in the documentation:
- Public SDK contract: delivery modes, outputs, errors, limits, privacy, reports and compatibility.
- Problem-first and similarity boundaries: prepared metrics, declared operations and construction guarantees.
- Canonical and optimized artifacts: profiles, lineage, validation and recommendation.
- IBM/Qiskit handoff: the boundary between logical artifacts and physical execution.
- Examples and tutorials: first workflow, ScoreModel and inspectable artifacts.
Support And Security
- General questions and defects: GitHub Issues
- Sensitive security reports: follow SECURITY.md; do not open a public issue
- Release history: CHANGELOG.md
- Roadmap: ROADMAP.md
License
The client SDK, examples, documentation and tests in this repository are licensed under the MIT License.
QDSV, QIntent and Qruba names and marks belong to their respective owners. The MIT License does not grant trademark rights.
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