README.md

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title: MAH Quantum Research Lab
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# ▣ MAH QUANTUM // COGNITIVE ARCHITECTURES

[![NVIDIA NGC](https://img.shields.io/badge/NVIDIA%20NGC-ORG%3A0963318590610147-76B900?style=flat-square&logo=nvidia&logoColor=white)](https://catalog.ngc.nvidia.com/orgs/0963318590610147)
[![CUDA](https://img.shields.io/badge/CUDA-12.x-0073B1?style=flat-square&logo=nvidia&logoColor=white)](https://developer.nvidia.com/cuda-toolkit)
[![TensorRT](https://img.shields.io/badge/TensorRT--LLM-Active-0073B1?style=flat-square)](https://github.com/NVIDIA/TensorRT-LLM)
[![Hopper](https://img.shields.io/badge/Arch-Hopper%20%7C%20Blackwell-5B4BCC?style=flat-square)](https://www.nvidia.com/en-in/data-center/hopper-architecture/)
[![MSME](https://img.shields.io/badge/MSME-Govt%20of%20India%20Verified-FF9933?style=flat-square)](https://udyamregistration.gov.in)
[![Scholars](https://img.shields.io/badge/MQ%20Scholars%20Cohort-42%20Active-blueviolet?style=flat-square)](https://workspace.mahquantum.tech)
[![Indexing](https://img.shields.io/badge/Indexing-Zenodo%20%7C%20OpenAIRE-success?style=flat-square)](https://research.mahquantum.tech)

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**MAH Quantum** is a Bengaluru-based deep-tech research organization and accelerated computing node. We architect decentralized cognitive AI pipelines, custom quantization frameworks (PTQ/QAT), and high-fidelity quantum emulation layers engineered to maximize compute density across modern GPU clusters. All enterprise infrastructure maps to NGC Org `0963318590610147` on Hopper and Blackwell silicon, bound natively to a cloud-only production staging mandate.

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### ⬡ RESEARCH VERTICALS

**`[01] Decentralized Cognitive AI`** — Fault-tolerant, distributed LLM inference pipelines. Multi-node serving architectures with dynamic load balancing across CUDA 12.x cluster topologies. Target: sub-50ms TTFT at 70B+ parameter scale.

**`[02] Quantization Frameworks`** — Custom PTQ and QAT pipelines with mixed-precision support (INT4/INT8/FP8). TensorRT-LLM integration with layer-wise sensitivity profiling and calibration tooling for Hopper Transformer Engines.

**`[03] Quantum Emulation`** — High-fidelity NISQ-era quantum circuit simulation on classical GPU backends. State-vector and tensor-network simulators with CUDA-accelerated gate kernels. Targeting 40+ qubit emulation at research scale.

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### ⬡ INFRASTRUCTURE & TOPOGRAPHY REGISTRY

```
HARDWARE_COMPUTE_STREAMS
├── NGC_ORG_ID        : 0963318590610147
├── TargetArch        : NVIDIA Hopper (H100) · NVIDIA Blackwell (B200)
├── CUDA              : 12.x
├── AccelEngines      : TensorRT-LLM · DeepStream Pipeline · cuDNN
└── Deployment        : NGC Verified Container Registry (100% Cloud-Native)

ENTERPRISE_NETWORK_TOPOGRAPHY
├── RootGateway       : https://mahquantum.tech (Governance & Corporate Root)
├── ScholarLMSPortal  : https://workspace.mahquantum.tech (Internal LMS Node)
├── ScholarlyIndex    : https://research.mahquantum.tech (CERN/EU Federated Repo)
└── CodeSandbox       : https://huggingface.co/mah-quantum

INTELLIGENT_GOVERNANCE_POLICIES
├── CodeEnvironment   : Cloud-First Staging Mandate (No Local Machine Execution)
├── Documentation     : Manual Faculty-Led AI Audit (<5% Algorithmic Tolerance)
├── EvaluationCadence : 1-Year Capstone Pipeline · 2-Hour Weekly Architecture Crucible
└── AuditTransparency : Shared Master Ledger (Real-time Dean & Director Telemetry)
```

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### ⬡ ACADEMIC PROVENANCE ENGINE

```
SCHOLARLY_INFRASTRUCTURE
├── DOIRegistrar      : Zenodo Automated Minting API (CERN Infrastructure)
├── DiscoveryIndex    : OpenAIRE Federated Network / EU Repository Core
└── SerialStatus      : E-ISSN Board Configuration Phase
```

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### ⬡ REPOSITORY INDEX

| Type | Description |
|---|---|
| `model` | Quantized LLM checkpoints — INT4/INT8/FP8 variants optimized for Hopper TE |
| `dataset` | Domain-adaptive pretraining corpora and instruction-tuning splits |
| `space` | Inference demos, quantization benchmarks, emulation visualizers |
| `toolkit` | PTQ calibration tools, profiling utilities, cluster benchmarking scripts |

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### ⬡ INSTITUTIONAL COHORT GOVERNANCE

MAH Quantum operates on a strict, non-spoon-fed corporate structure modeled after top-tier global research labs. Candidates are assigned complex system engineering and deep literature tracks monitored via a four-tier validation layer:

**`[A] Production-First Mandate`** — Local machine development loops are banned. All system architectures must deploy as microservices, exposing functional APIs monitored via centralized infrastructure telemetry.

**`[B] Manual Faculty Vetting`** — University faculty members manually audit all documentation pipelines to enforce parallel institutional understanding. Hard threshold: `<5%` AI text generation tolerance.

**`[C] Master Ledger Matrix`** — Every development sprint, system metric, and grading evaluation is logged into a secure Master Spreadsheet, granting direct, transparent auditing access to the university Dean and institutional Directors.

**`[D] Root Admin Veto`** — MAH Quantum leadership executes random spot-checks on staging repositories, retaining absolute final overwrite and candidate offboarding authority to guarantee prestige integrity and eliminate grade inflation.

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<sub>MAH Quantum Research Institute · Bengaluru, India · MSME Registered · E-ISSN Config Phase · <a href="https://mahquantum.tech">mahquantum.tech</a></sub>
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