README.md

---
license: other
license_name: qgi-commercial-model-license-v1
license_link: LICENSE.md
language:
  - en
pipeline_tag: feature-extraction
inference: false
tags:
  - quantum-augmented-generation
  - qag
  - q-prime
  - embedding
  - sentence-embedding
  - feature-extraction
  - rule-bearing-text
  - compliance
  - regulated-ai
  - policy
  - conflict-detection
  - audit-trail
  - born-rule
  - hilbert-space
  - quantum-hypergraph
  - qhg
  - managed-api
  - gated-model
gated: manual
extra_gated_heading: "Request Q-Prime evaluation access"
extra_gated_description: |
  Q-Prime is a commercial quantum-structured embedding model for regulated AI.
  Model weights, adapters, and training data are **not distributed**.
  Approved evaluation requests receive a time-boxed API key for non-production
  use under the QGI Commercial Model License v1.0 §3 (90-day evaluation grant).
  Production use requires a Permitted Commercial License — `contact@qgi.dev`.
extra_gated_fields:
  Organization: text
  Country of use: country
  Intended application: text
  Production or evaluation:
    type: select
    options:
      - "Evaluation (non-production)"
      - "Production (commercial license required)"
  I have read and agree to the QGI Commercial Model License (v1): checkbox
  I will not use Q-Prime to automate safety-critical decisions without qualified human review: checkbox
extra_gated_button_content: "Request evaluation access"
extra_gated_prompt: |
  QGI reviews all requests manually. Evaluation access is time-boxed and
  non-production. Target SLA: 3 business days.
---

# Q-Prime — QGI Quantum-Structured Embedding Model

> **No weights on this repo. Q-Prime is a managed API.**
> This Hugging Face page exists for discovery, procurement, and citation.
> There is no `.safetensors`, `.bin`, `.onnx`, `.gguf`, or any other loadable
> artefact here — by design. Access is granted via API key after approval.
> Jump to [How to access](#how-to-access) ↓

**Q-Prime** is a quantum-structured embedding model purpose-built for regulated AI. It powers the **QAG engine** — Quantum-Augmented Generation — QGI's successor category to classical RAG for applications that cannot afford to hallucinate.

Q-Prime is accessed **as a managed API**. Weights, adapters, tokenizer, and training data are **not distributed**. See [How to access](#how-to-access) below.

> **License:** QGI Commercial Model License v1.0 — evaluation access available on request via the **Request access** button above; paid commercial license required for production. See [LICENSE.md](./LICENSE.md) for full terms. Licensing: `contact@qgi.dev`.

---

## What Q-Prime does

Rules and regulated text carry structure that classical embeddings discard. A clause holds several meanings at once; it is correlated with other clauses in ways that reinforce, condition, or contradict. A cosine-based retriever flattens all of that into a single point in a vector space and loses the signal that matters.

Q-Prime is built on the opposite premise. It **finds entangled superpositions in rules and text** and emits a **quantum-structured representation** that keeps them intact. The representation is more compact than a classical embedding — relational structure lives in the state itself, not padded into extra dimensions — and it exposes parameters that cosine similarity cannot see: polarity, scope, conditions, obligation, and cross-rule dependency.

Q-Prime feeds a pipeline — the **QAG engine** — that reads this structure at inference time. Interference between related representations produces a **signed signal**: same-polarity related statements reinforce, opposite-polarity related statements cancel. The sign is the decision. Contradictions that differ only by a negation — "must report" vs "must not report" — become separable, and the same mechanism surfaces scope conflicts, conditional overrides, and other parameters that classical retrieval averages away.

## Real quantum formalism on classical hardware

Q-Prime uses the mathematical apparatus of quantum mechanics — Hilbert-space states, superposition, interference, the Born rule $P(\text{outcome}\,|\,\psi) = |\langle\text{outcome}\,|\,\psi\rangle|^2$ — evaluated on commodity GPUs. It is not a quantum-hardware model and it is not "quantum-inspired". The operator algebra is the real one, the Born rule is the real one, and the probabilities it emits are calibrated in the same sense that physical Born-rule measurements are.

This is what lets Q-Prime expose a **Born-rule classifier** $\arg\max_c |\langle c\,|\,\psi\rangle|^2$ as a zero-shot categorization primitive, and a **signed interference signal** $\mathrm{polarity}(a,b)\cdot\langle a\,|\,b\rangle$ as a conflict-detection primitive.

## Intelligence signals (public API surface)

Q-Prime feeds the QAG engine with five first-class signals:

- **Relevance** — which rules apply to a given context.
- **Overlap** — where rule conditions intersect.
- **Conflict** — where rules produce contradictory outcomes.
- **Redundancy** — duplicate or near-duplicate rules.
- **Predicate extraction** — the condition component of a rule.

Coverage, coherence, and topology signals exist internally and will ship in later releases.

---

## Intended use

Q-Prime is intended for use by:

- Regulated-industry engineering teams embedding rules, policies, contracts, and case documents.
- Compliance and audit functions running continuous rule-to-rule conflict detection across versioned policy sets.
- Regulated-news and research desks synthesizing multiple sources where the sign of the claim matters.
- Risk and model-governance leaders deploying an embedding layer whose failure mode is explainable, not statistical.
- Agent builders implementing conflict-aware long-term memory and context engineering.

Q-Prime is **not** intended for:

- General-purpose open-web retrieval or low-stakes Q&A.
- Non-English corpora (current release is English-only).
- Autonomous decisions that materially affect an individual's legal rights, employment, housing, credit, healthcare access, education, or liberty — except under a certified pipeline that includes qualified human review.

See [License §5 (Responsible Use)](./LICENSE.md) for the binding language.

---

## How to access

Q-Prime is distributed exclusively as a managed API. There are three access paths.

### 1. Evaluation access (researchers, engineers, non-production)

Click **Request access** above, or email `contact@qgi.dev`. Evaluation is governed by [LICENSE.md §3](./LICENSE.md) (90-day grant, non-production only, academic use permitted). Target SLA: 3 business days.

### 2. OpenRouter (pay-per-call)

Q-Prime will be listed on OpenRouter as part of the QAG engine progressive beta. Listing status and pricing are announced at [qgi.dev](https://qgi.dev).

### 3. Enterprise (production, SLA, audit, dedicated endpoints)

`contact@qgi.dev`. Tiers: Startup, Growth, Enterprise, OEM / Channel.

**General availability of the full QAG engine is targeted for 21 June 2026.** Q-Prime is available to selected customers in progressive beta before that date.

### Quickstart (after you have an API key)

```python
# pip install requests
import os, requests

API_URL = "https://api.qgi.dev/v1/qprime/embed"
API_KEY = os.environ["QGI_API_KEY"]

resp = requests.post(
    API_URL,
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "inputs": [
            "A regulated entity must report any incident within 72 hours.",
            "A regulated entity must not report an incident if law-enforcement investigation is active.",
        ],
        "tasks": ["relevance", "conflict", "predicate"],
    },
    timeout=30,
)
resp.raise_for_status()
out = resp.json()
# out["conflict"][0][1] > 0  →  the two clauses conflict on polarity + scope
```

A live demo is at [QGI-dev/q-prime-demo](https://huggingface.co/spaces/QGI-dev/q-prime-demo).

---

## What you do **not** get

To set expectations before first contact:

- **No weights download.** Q-Prime weights, adapters, and supporting parameters are not distributed. Redistribution requires a separately negotiated license.
- **No training recipe.** Data curation, training procedure, and internal evaluation methodology are confidential trade secrets ([LICENSE.md §2.2](./LICENSE.md)).
- **No architecture disclosure beyond the papers.** Architectural details ship per the release cadence in the QAG paper series.

We are aware this is unusual for a model card on Hugging Face. Q-Prime is a commercial product, not an open research artifact. This card exists so developers and procurement teams can evaluate fit, not so the model can be cloned.

---

## Evaluation

A headline result accompanies this card: on QGI's regulatory-conflict benchmark, Q-Prime lifts rule-conflict F1 from **0.000** (with a leading general-purpose embedding) to near-perfect on in-distribution data. Full evaluation methodology, out-of-domain results, multiple-backbone comparisons, throughput/latency numbers, and the full benchmark suite are released under evaluation agreement — `contact@qgi.dev` — and published in the forthcoming evaluation paper (Paper G in the QAG series).

Numbers on this card will be updated once Paper G is public.

---

## Accompanying papers (QAG series)

| Paper | Focus |
|---|---|
| [Paper A — Quantum-Augmented Generation (QAG)](https://arxiv.org/abs/TBD) | Canonical engine paper. Core formalism, signals, architecture. |
| [Paper B — Purpose-Built Embedding Models for Rule-Bearing Text](https://arxiv.org/abs/TBD) | Position paper motivating Q-Prime. |
| [Paper C — Conflict-Aware Memory for AI Agents](https://arxiv.org/abs/TBD) | Long-term memory using QAG primitives. |
| [Paper D — A Born-Rule Classifier](https://arxiv.org/abs/TBD) | Zero-shot, calibrated categorization method note. |
| [Paper E — Quantum HyperGraph (QHG)](https://arxiv.org/abs/TBD) | First-class data model for rule-bearing knowledge. |
| [Paper F — Beyond Retrieval-Augmented Generation](https://arxiv.org/abs/TBD) | 2026 landscape review. |
| Paper G — Empirical Evaluation | Held under evaluation agreement; release with this card's numbers update. |

Each arXiv ID is added to this card's `arxiv:` YAML as it publishes.

---

## Citation

```bibtex
@misc{sammane2026qprime,
  title        = {Q-Prime: A Quantum-Structured Embedding Model for Regulated AI},
  author       = {Sammane, Sam and {Quantum General Intelligence, Inc.}},
  year         = {2026},
  howpublished = {Model card, \url{https://huggingface.co/QGI-dev/q-prime}},
  note         = {QAG engine documentation; accompanying paper series on arXiv}
}
```

Academic work using Q-Prime must cite per [LICENSE.md §8](./LICENSE.md).

---

## Responsible use

Q-Prime may not be used to:

- Automate safety-critical decisions (as enumerated above) without qualified human review.
- Circumvent legal or regulatory obligations in the user's jurisdiction.
- Misrepresent the user's compliance posture to a regulator, counter-party, or auditor.
- Train a model intended to compete with Q-Prime, QAG, Neural Symbolic Agents, or any Qualtron model.

See [LICENSE.md §5](./LICENSE.md) for the binding language.

---

## Contact

| Need | Where |
|---|---|
| Evaluation access, API keys, documentation | `sam@qgi.dev` |
| Commercial license, enterprise pilots, SLA, support | `sam@qgi.dev` |
| QAG engine waitlist (GA 21 June 2026) | [qgi.dev](https://qgi.dev) |
| Partnership (cloud providers, hyperscalers, channel) | `partner@qgi.dev` |
| Press and analyst relations | `press@qgi.dev` |
| Security disclosure | `security@qgi.dev` |

---

## Company

**Quantum General Intelligence, Inc.** — Delaware corporation, founded 2025.

Website: [qgi.dev](https://qgi.dev) · Hugging Face: [QGI-dev](https://huggingface.co/QGI-dev) · GitHub: [Quantum-General-Intelligence](https://github.com/Quantum-General-Intelligence)

© 2025–2026 Quantum General Intelligence, Inc. All rights reserved.
"Q-Prime", "QAG", "Quantum-Augmented Generation", "QGI", "Neural Symbolic Agents", and "Qualtron" are trademarks of Quantum General Intelligence, Inc.