QDSV Bridge Developer Preview

Current package version: 0.5.0.

Documentation site: https://qdsvquantum-afk.github.io/qdsv-bridge/

QDSV Bridge converts supported semantic problem specifications into executable quantum circuit artifacts or expert construction packages. It is a lightweight Python client SDK built on QDSV - Quantum Declarative Semantic Value.

Bridge never labels an incomplete artifact as a completed circuit. If the accepted problem can be constructed with the currently supported capabilities and resource limits, Bridge returns the circuit. Otherwise it returns a bounded expert construction package or an explicit capability error; it does not invent a circuit or embed a precomputed answer.

The public developer preview is available without an API key:

from qdsv_bridge import QDSVBridgeClient

client = QDSVBridgeClient()  # Uses https://api.qdsv.cloud/api

Use a private/local Docker node only when you are running QDSV privately:

client = QDSVBridgeClient.local()  # Uses http://localhost:18080/api

QDSV Bridge is not a template selector and not a free arbitrary circuit generator. Its core rule is:

do not force the problem into a prefabricated circuit;
derive the circuit or construction inputs from the semantic problem specification.

Choose A Delivery Mode

Bridge has one SDK with four delivery modes. Choose the mode by the output you need, not by creating a different kind of project.

Choose how much control you want over the returned artifact. A legacy family value may be retained as a descriptive label, but operation capabilities determine whether a circuit can be generated:

If you need...	Use	Python helper	Main output
A simpler starting point without designing circuits	Bridge Use	client.generate(spec)	Canonically materialized circuit, evidence, guidance and ready-to-run example
An inspectable OpenQASM/Qiskit artifact	Bridge Build	client.build(spec)	Executable QASM/Qiskit artifact, public oracle contract, stable public IR summary, actual metrics and digests
Expert construction inputs before final circuit materialization	Bridge Expert Prepare	client.prepare(spec)	Semantic construction inputs without forcing a final circuit
Expert evaluation of a materialized artifact	Bridge Expert Evaluate	client.evaluate(spec)	The actual materialization, construction evidence and clearly labeled non-materialized alternatives

If you are not sure where to start, use:

result = client.generate(spec)  # basic

If you want to inspect or route the artifact into Qiskit, Braket or another OpenQASM-oriented workflow, use:

result = client.build(spec)  # intermediate/developer

Expert users have two separate routes: prepare for semantic construction ingredients, and evaluate for the actual materialization evidence plus clearly labeled conceptual alternatives.

5 Minute Quickstart

Install the SDK:

pip install qdsv-bridge

For the optional Qiskit artifact inspection path:

pip install "qdsv-bridge[qiskit]"

The Qiskit extra is intentionally version-capped to the current supported major series (qiskit>=2,<3) to follow Qiskit Ecosystem compatibility guidance.

Build a QASM3 artifact from a compact semantic spec. No key is required in the public preview:

from qdsv_bridge import QDSVBridgeClient

client = QDSVBridgeClient()

spec = {
    "state_space": {
        "kind": "finite_candidates",
        "candidate_count": 2,
        "candidate_id": "candidate",
    },
    "signals": ["eligibility_score"],
    "prepared_candidates": [
        {"eligibility_score": 0},
        {"eligibility_score": 1},
    ],
    "goal": {
        "kind": "marking",
        "threshold": 1,
        "criteria": [
            {"signal": "eligibility_score", "importance": 1, "priority": 1}
        ],
    },
    "target": {
        "format": "qasm3",
        "backend_family": "qiskit",
    },
    "limits": {
        "max_qubits": 8,
        "max_depth": 160,
    },
}

result = client.generate(spec)  # basic-user mode

print(result["status"])
print(result["bridge_mode"])
print(result["circuit_origin"])
print(result["artifact"]["format"])
print(result["materialization_evidence"]["formula_materialized_in"])
print(result["materialization_evidence"]["candidate_precomputed"])
print(result["construction_verification"])

For a developer-oriented package with oracle spec, IR summary, digests and preservation evidence:

result = client.build(spec)

print(result["editable_artifacts"]["oracle_spec"])
print(result["editable_artifacts"]["ir_summary"])
print(result["digests"])

Colab Notebooks

Use these notebooks when you want to try Bridge without setting up a local project:

Notebook	Flow
Open 01 Semantic Candidate Marking	bounded_semantic_marking -> QASM3 artifact -> Bridge Report
Open 02 Predicate Construction Inputs	predicate_marking -> expert construction inputs -> Bridge Report
Open 03 Semantic Signal Classification	semantic_signal_classification -> QASM3 artifact -> Bridge Report
Open 04 IBM/Qiskit Artifact Demo	Problem-first spec -> QASM3 artifact -> Qiskit inspection/simulation -> Bridge Report
Open 05 AWS Braket OpenQASM Demo	Problem-first spec -> OpenQASM artifact -> Braket LocalSimulator -> Bridge Report

Each notebook follows the same route:

problem spec -> Bridge artifact -> shareable report

IBM/Qiskit Workflow

For Qiskit-oriented users, see the public integration guide:

• QDSV Bridge for IBM/Qiskit Workflows
• Open 04 IBM/Qiskit Artifact Demo

Amazon Braket OpenQASM Workflow

For users of the tested Amazon Braket LocalSimulator conversion workflow, see:

• QDSV Bridge for Amazon Braket OpenQASM Workflows
• Amazon Braket Community Submission Path
• Open 05 AWS Braket OpenQASM Demo

QDSV Bridge exports an OpenQASM artifact compatible with the tested Amazon Braket LocalSimulator conversion workflow and includes to_braket_openqasm() for its bounded gate-name/definition conversion. This is not an official Amazon Braket integration and does not include managed AWS hardware execution.

OpenQASM-First Interoperability

For a provider-neutral view of the Bridge artifact handoff, see:

• QDSV Bridge OpenQASM-First Interoperability

Bridge uses OpenQASM as a public artifact boundary between controlled semantic problem specifications and framework-specific workflows such as Qiskit or Amazon Braket.

Default cloud endpoint:

https://api.qdsv.cloud/api

Private/local Docker endpoint:

client = QDSVBridgeClient.local()

What This Is

• A client SDK for QDSV Bridge.
• A controlled interface for requesting problem-derived quantum circuit artifacts.
• A way to request QASM/Qiskit artifacts, public construction contracts or expert construction inputs.
• A way to generate shareable Bridge Reports in JSON, Markdown or HTML.
• A bridge for users who need circuit ecosystems but do not want to start from prebuilt templates.

What This Is Not

• It is not the private QDSV Runtime.
• It is not a bulk data processing SDK.
• It is not a hardware execution SDK.
• It is not an arbitrary circuit generator.
• It is not a selector of prefabricated circuit templates.
• It does not expose the private runtime, internal compilation or optimization rules, private backend adapters, secrets or production configuration.

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 bounded semantic problem and checks that every required capability and resource is available before a circuit is claimed.

semantic problem spec
-> capability and resource validation
-> executable circuit or expert construction package
-> auditable report

For users who want a simpler starting point, Bridge can generate executable problem-derived circuits when the specification compiles completely to a reversible semantic formula. It refuses to present incomplete blueprints or classically enumerated answer oracles as completed circuits.

For expert constructors, Bridge can also return the key semantic inputs needed to design a custom circuit without forcing a final circuit.

Bridge Modes

QDSV Bridge is one SDK with multiple output depths. The modes do not change the core principle: a circuit is delivered only after the complete supported construction path succeeds.

API	Commercial name	Intended user	What Bridge returns
generate() / use	Bridge Use	Basic user who needs a circuit without designing its quantum core.	Ready-to-use canonical circuit, dependencies, expected inputs, measurement meaning, actual resources and a minimal loading example. Execution and integration remain with the user.
build() / build	Bridge Build	Intermediate user who understands Qiskit, QASM or quantum workflows.	Canonical materialized circuit plus executable QASM/Qiskit, oracle spec, IR summary, actual qubits/depth and digests.
prepare() / expert_prepare	Bridge Expert Prepare	Expert constructor who wants to design a custom circuit.	Validated semantic specification, ProblemSpec/IR, oracle contract, predicates, variables, constraints, capability gaps, encoding/measurement guidance, limits and digests. It does not force a final circuit.
evaluate() / expert_evaluate	Bridge Expert Evaluate	Expert evaluator who wants the full construction evidence.	The actual canonical materialization and resources. Any other design is labeled conceptual until it is independently materialized; Bridge does not claim an execution comparison.

Python helpers:

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

Explicit mode:

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
qdsv-bridge report spec.json --mode build --format markdown --output bridge_report.md

Every export response should include traceability metadata:

{
  "mode": "use | build | expert_prepare | expert_evaluate",
  "artifact_type": "qasm3",
  "circuit_origin": "qdsv_canonical_problem_ir_materializer",
  "materialization_evidence": {
    "formula_materialized_in": "quantum_circuit",
    "execution_performed": false,
    "classical_scan": false,
    "candidate_precomputed": false,
    "answer_leakage": false,
    "precomputed_result_table": false
  },
  "construction_verification": {
    "status": "passed",
    "scope": "semantic_specification_to_circuit_construction",
    "execution_performed": false
  },
  "warnings": [],
  "digests": {}
}

Examples By User Type

Basic user:

result = client.generate(spec)
print(result["circuit_origin"])          # qdsv_canonical_problem_ir_materializer
print(result["circuit"]["status"])       # materialized_from_canonical_problem_ir
print(result["ready_to_run_example"]["dependencies"])
print(result["ready_to_run_example"]["measurement_contract"])
print(result["ready_to_run_example"]["resources"])

Intermediate developer:

result = client.build(spec)
print(result["artifact"]["format"])
print(result["editable_artifacts"]["oracle_spec"])
print(result["editable_artifacts"]["ir_summary"])
print(result["digests"])

Expert constructor:

inputs = client.prepare(spec)
print(inputs["expert_inputs"]["construction_status"])
print(inputs["expert_inputs"]["missing_capabilities"])
print(inputs["expert_inputs"]["encoding_suggestions"])
print(inputs["expert_inputs"]["measurement_suggestions"])

Expert evaluator:

comparison = client.evaluate(spec)
print(comparison["materialization_variants"])
print(comparison["construction_alternatives"])
print(comparison["comparison"])

How Bridge Checks The Circuit Construction

Bridge verifies the construction path before returning a completed circuit:

1. It validates and normalizes the semantic specification.
2. It builds one canonical public problem representation and operation plan.
3. It rejects the circuit request if any required operation lacks a certified implementation.
4. It checks the reversible compute/decision/uncompute contract and rejects embedded answer tables or precomputed winning candidates.
5. It materializes the concrete circuit, measures its actual qubits, depth and operation counts, and enforces the requested/public resource limits.
6. It links the specification, IR, formula/oracle and artifact with digests.

The response exposes these checks in construction_verification. This verifies that the delivered artifact was constructed from the accepted semantic specification through the supported QDSV path. It does not prove that the user modeled the real-world problem correctly, that the supplied data is true, or that a simulator/QPU produced a correct business result. Bridge does not run the circuit; execution, provider selection, credentials, shots, cost and result validation remain with the user.

Advanced Technical Boundary

Internally, the supported path uses a typed operation graph and a reversible intermediate representation before concrete circuit generation. These stages are mentioned here only to define the verification boundary: the public SDK does not expose the private reversible IR, lowering implementation, optimization rules or backend adapters. Public responses contain only stable summaries, capability identifiers, resource evidence and digests.

Operation Capabilities

Circuit construction is selected from the required operations and available capabilities, not from a problem-family name. Query the current catalog with:

catalog = client.capabilities()
print(catalog["operation_capabilities"])

Each physical/composite operation reports whether it is semantically supported, graph-composable, verified for circuit generation and QASM loading, and whether a resource profile is available. Verification is profile-specific; it is not a claim that the same operation is materializable in every possible context.

client.capabilities() uses /api/bridge/capabilities. The previous client.families() helper remains only as a compatibility alias; family labels do not select a compiler, set physical capacity or promise circuit delivery.

The canonical bounded compiler supports predicates composed from these public operations:

• values and arithmetic: field, add, sub, mul, div, safe_div, ratio, percent, mod, abs, abs_diff, squared_diff, min, max, clip, sum_fields, mean_fields, round, floor, ceil and sign;
• decisions and sets: eq, ne, lt, lte, gt, gte, between, outside, within_tolerance and in_set;
• logic and missing-value handling: and, or, xor, not, is_null, not_null, coalesce and default_if_invalid;
• bounded similarity and vectors: similarity, vector, vector_similarity and weighted_sum;
• ScoreModel v2 flat and hierarchical importance-priority aggregation, including signed contextual adjustments, normalization and penalties.

Every operation above is tested through the same canonical path from semantic specification to QASM loading and ideal circuit execution. This is a bounded finite-domain guarantee, not an unlimited resource guarantee: a valid specification can still be rejected when its required qubits, depth, function states or artifact size exceed the declared service limits.

ScoreModel v2 Decision Capability

ScoreModel v2 is the broadest decision-oriented circuit path currently exposed through Bridge. It is not tied to one industry or to similarity alone. It materializes bounded multi-criteria decision predicates over finite candidates through the canonical QDSV operation compiler.

ScoreModel v2 accepts two complementary value routes:

Value route	What the user provides	Typical sources
Prepared numeric metric	Any finite numeric value that fits the declared numeric contract, together with optional provenance	Probability, normalized distance, risk metric, correlation, externally calculated similarity, model output or domain measurement
QDSV numeric expression	Prepared numeric fields plus a bounded operation graph supported by the ScoreModel v2 physical profile	Direct values, bounded arithmetic, absolute or squared difference and scalar numeric similarity

An externally produced metric remains an input: Bridge does not claim to have computed it. This lets users bring a value from another model or domain process while QDSV materializes the bounded decision operation. QDSV can also compute scalar numeric similarity inside its specialized circuit profile. Bounded vector similarity is available through the canonical general profile when its complete finite-domain materialization fits the declared resource limits. A separately computed finite similarity value can also be supplied as a prepared metric, in which case Bridge records it as an input rather than claiming to compute it.

The implemented ScoreModel v2 capability includes:

• flat multi-term decisions;
• hierarchical decisions with independent importance and priority at each block;
• non-negative term and block importance and priority values;
• signed contextual adjustments over bounded adjustment values;
• term aggregation, normalization and zero-mass protection;
• flat/global and hierarchical block/global penalty handling;
• exact rational semantic evaluation followed by declared fixed-point output, deterministic rounding and overflow rejection;
• eq, ne, lt, lte, gt and gte threshold decisions;
• bounded value expressions using fields, constants, addition, subtraction, multiplication, division, safe division, modulo, absolute value, absolute difference, squared difference and scalar numeric similarity when accepted by the current capability assessment;
• candidate-independent reversible formula synthesis, decision marking, measurement and explicit uncompute evidence;
• optional bounded Grover amplification inside the certified ScoreModel v2 materialization, without inferring winning candidates through a classical answer scan. This is not a general Grover recipe for arbitrary problems.

Public ScoreModel specifications use user-oriented decision names:

term = {
    "value": prepared_metric,
    "importance": 2,
    "priority": 3,
}

importance expresses the relative contribution of a factor. priority expresses its declared urgency or severity. The compiler uses their product as the factor applied in both the weighted numerator and normalization mass. A zero value removes that term or block from the aggregate; priority is not circuit execution order or precedence. The legacy names weight and criticality remain accepted for compatibility, but new SDK code and public Bridge responses use importance and priority. QDSV keeps any canonical mathematical naming internal to the operation compiler.

This supports practical finite-candidate workflows such as:

• eligibility, approval and compliance screening;
• multi-criteria candidate selection by threshold;
• risk-benefit and cost-value assessment;
• project, supplier, customer, hypothesis or alternative evaluation;
• contextual decisions where the same observed value must be adjusted by declared circumstances;
• hierarchical decisions that combine local block assessments into one global acceptance predicate.

These are bounded threshold-based circuit-delivery workflows. Bridge only claims the operations and materialization profiles listed by its public capability contract; capabilities outside that contract are not inferred or substituted.

ScoreModel v2 is available through a canonical problem_spec. Bridge returns the materialized circuit and a public evidence passport. It does not expose private compiler implementation, internal optimization rules, intermediate candidate scores or precomputed answers. The public profile is only a capability identifier; the evidence also reports actual qubit and depth metrics.

The current physical synthesis is bounded by the declared max_input_qubits and max_function_states, as well as Bridge payload, QASM, qubit and depth limits. Resource cost can grow rapidly with the number and precision of prepared fields. Bridge stops after delivering the circuit artifact or expert construction inputs. It does not execute the circuit or validate the problem result; hardware validation is not part of the Bridge delivery contract. The user chooses the simulator or provider, supplies the execution resources and validates the resulting behavior. See examples/score_model_v2.py for a bounded circuit-delivery example.

Bridge rejects unknown operations and any construction that cannot be materialized within the active capability and resource contract. It never substitutes a different operation or returns a circuit labeled as complete when the requested semantic graph was not materialized.

Legacy family values such as bounded_semantic_marking remain accepted as compatibility labels. They do not choose the compiler, alter the generated circuit or promise support.

Legacy family labels remain accepted only as optional metadata:

legacy_labeled_spec = {**spec, "family": "bounded_semantic_marking"}

The label does not select the compiler, alter the circuit or promise support.

What It Exports

What is exposed depends on the delivery mode:

Mode	Public artifact boundary
generate()	Circuit, loading guidance, measurement meaning, actual resources and construction evidence. No public IR or lowering details.
build()	Editable QASM/Qiskit artifact, public oracle contract, stable public IR summary, resources and digests. No reversible IR.
prepare()	Public construction contract, semantic requirements, capability gaps and design guidance. No private compiler plan.
evaluate()	Actual materialization evidence and conceptual alternatives. No private selection heuristics.

The public oracle_spec never contains answer tables, private function rows or winning candidates. A public profile value is an identifier and version only, not an implementation disclosure.

Bridge Report

Bridge Report turns the same auditable export package into a document users can share, attach to technical notes or keep as reproducibility evidence.

Supported report formats:

• json
• markdown
• html

Python:

report = client.report(spec, mode="build", format="markdown")
print(report["content"])

CLI:

qdsv-bridge report spec.json --mode build --format markdown --output bridge_report.md
qdsv-bridge report spec.json --mode build --format html --output bridge_report.html
qdsv-bridge report spec.json --mode build --format json --output bridge_report.json

Each Bridge Report includes:

1. Problem and deliverable contract.
2. Delivery mode and the legacy family label, only when supplied.
3. Construction verification and canonical materialization evidence.
4. Generated executable QASM/Qiskit content when requested.
5. Circuit policy and template-use status.
6. Digests.
7. Resource limits and warnings.
8. Reproducibility metadata.

Bridge Report is the public trust document for Bridge: it makes clear what was derived, what was preserved, what was warned about and which digests identify the exported artifacts.

For use and build, circuit-oriented targets return a circuit only when canonical QDSV materialization succeeds, together with formula/oracle digests and actual circuit metrics.

Bridge does not insert placeholder oracle comments and call them completed circuits. Incomplete specifications must use expert_prepare or add computable semantic inputs.

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.

Public Endpoints

The SDK calls the QDSV API:

• GET /api/bridge/capabilities
• GET /api/bridge/families (compatibility alias)
• POST /api/bridge/validate
• POST /api/bridge/compile
• POST /api/bridge/explain
• POST /api/bridge/export
• POST /api/bridge/report

Default cloud endpoint:

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

Local/private Docker endpoint:

client = QDSVBridgeClient.local()

client.capabilities() is the recommended name for the informational catalog. families() and /bridge/families remain compatibility aliases. Developer Preview value-producing operations can be used without an API key and are limited by rate limits, payload limits and a demo quota per caller bucket.

Resource And Multi-User Limits

QDSV Bridge accepts compact semantic problem specifications and small prepared numeric signal tables, not raw datasets.

Do not send:

• full CSV files;
• raw datasets;
• thousands of rows;
• sensitive records;
• training data;
• hardware execution requests.

Send:

• semantic problem_spec or bounded state space;
• finite state-space size;
• prepared signal or variable names;
• threshold decision goal or predicate supported by the current compiler;
• target format;
• qubit/depth limits;
• compact prepared_candidates containing only the bounded numeric signals required by the formula, when requesting a coherent circuit;
• or a canonical problem_spec containing the supported operations needed to construct the circuit.
• unsupported predicates may be sent to expert_prepare; they are not converted into circuits from precomputed satisfying states.

Public API minimum limits:

Limit	Default
Max spec payload	64 KB
Max QASM / circuit artifact payload	256 KB
Max compile time	5 seconds
Max export time	10 seconds
API key required for	Not required by default in Developer Preview
Monthly demo quota	100 compilations/exports/reports per caller bucket
Raw data payloads	Not allowed; compact prepared numeric signals are allowed
Hardware execution	Not available from Bridge SDK
Semantic service boundary	4096 finite candidates / 64 named signals
Compiler v1 coherent boundary	16 candidates / 20 prepared-signal qubits
Default requested circuit ceiling	256 qubits / depth 1,000,000

The semantic service boundary is not a circuit-size promise. Actual circuit resources depend on the complete operation graph, precision, registers, reversible arithmetic, ancillas and uncompute.

Default API rate limits may be configured by the QDSV deployment:

• capabilities: 60/minute (families uses the same limit as a compatibility alias)
• validate: 30/minute
• compile: 20/minute
• explain: 20/minute
• export: 10/minute
• report: 10/minute

Quotas, authentication requirements and rate limits are configurable by each deployment. The public preview may apply different limits from a private or partner deployment.

Raw dataset payloads are rejected:

{
  "detail": {
    "error_code": "E_BRIDGE_RAW_DATA_NOT_ALLOWED",
    "message": "Raw datasets are not allowed in Bridge specs. Provide semantic signals, predicates, candidates, or summarized problem structure instead.",
    "detail": {
      "forbidden_fields": ["rows"]
    }
  }
}

Examples And Roadmap

• Examples
• Roadmap
• QDSV model site
• Qruba Cloud
• PyPI

Current public artifact targets are problem_spec, ir, oracle_spec, qasm2, qasm3 and qiskit_blueprint.

PennyLane, Azure Quantum/Q#, Cirq, VS Code, TypeScript clients, Amazon Braket managed hardware execution and packaged NISQ recipes such as QUBO/QAOA/VQE/Grover are planned integrations or higher-level examples. They should not be described as current public Bridge capabilities.

Important Boundaries

QDSV Bridge does not expose the private runtime, internal compilation or optimization rules, private backend adapters, secrets or production configuration. It also does not provide unrestricted circuit generation or arbitrary Python execution.

Product principle:

semantic problem specification
-> capability and resource validation
-> executable circuit materialization or expert inputs
-> auditable export

Not:

any input -> arbitrary circuit

Open SDK, Private Runtime

This repository contains the MIT-licensed Python client SDK, examples, documentation and tests. It does not contain the private runtime, internal compilation rules, private backend adapters, 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.