Quantum Calibration and Observable Processing Model (QCOP/M)
Model Summary
| Total Parameters | ~40B total, 5B active per token (MoE sparse activation) |
| Architecture | Mixture-of-Experts Vision-Language Model (MoE VLM) |
| Experts | 384 experts, 16 active per token |
| Context Length | 396,488 tokens |
| Precision | BF16 |
| Input | Image (PNG, JPEG) + Text |
| Output | Text (technical analysis, conclusions, parameter extraction) |
| Training | Two-phase sequential SFT (72.5K entries) |
| Minimum vRAM | 24GB (Custom runtime and Modded firmware) |
| Release Date | April 20, 2026 |
| License | MIT |
| Paper(-s): | A Unified Framework for Calibration, Decoding, and Resource-Aware Processing in Hybrid Quantum-Classical Machine Learning — Tunjay Akbarli (April 2026) |
Description
The QCOPM analyzes quantum computing calibration experiment plots and generates structured technical text across six analysis question categories. QCOPM was developed by Tunjay Akbarli as a quantum calibration vision-language model. This model is ready for commercial/non-commercial use.
Deployment Geography: Global
Quick Start
Suggested inference settings: temperature=0.2, max_tokens=16384.
Use Case
Quantum computing researchers, calibration engineers, and developers can use this model to analyze experiment plot images and generate technical descriptions, experimental conclusions, significance assessments, fit quality evaluations, parameter extractions, and experiment success classifications. Model outputs should be validated by domain experts before acting on experimental conclusions.
Benchmark Results
| Question Type | Ising Cal-1 | Qwen3.5-35B base | QCOPM |
|---|---|---|---|
| Q1 Technical Description | 87.8 | 86.8 | 91.2 |
| Q2 Experimental Conclusion | 67.1 | 39.9 | 78.4 |
| Q3 Experimental Significance | 64.7 | 45.7 | 75.9 |
| Q4 Fit Quality Assessment | 90.5 | 52.7 | 94.1 |
| Q5 Parameter Extraction | 62.5 | 57.8 | 70.3 |
| Q6 Experiment Success | 75.3 | 50.6 | 84.6 |
| Overall | 74.7 | 55.5 | 82.4 |
Analysis
- State-of-the-Art Performance: QCOPM outperforms Ising Cal-1 by an average of 7.7 points overall.
- Experimental Reasoning: The most significant gains are observed in Experimental Conclusion (Q2) and Significance (Q3), where QCOPM demonstrates a deeper understanding of scientific context.
- Data Precision: In Parameter Extraction (Q5), QCOPM achieves a score of 70.3, setting a new baseline for high-accuracy numerical retrieval in technical documentation.
Methodology
Models were tested on a closed-set of 1,200 technical queries requiring multi-step reasoning, mathematical fit evaluation, and experimental validation. Each score represents the percentage of correctly processed tasks as verified by domain experts.
Model Architecture
Architecture Type: Mixture-of-Experts Vision-Language Model (MoE VLM)
Network Architecture: Integrated vision encoder for experiment plot images combined with the MoE language model for autoregressive text generation.
Number of model parameters: ~40B total parameters, ~5B active per token (256 experts, 8 active)
Training Methodology
Phase 1 — ICL-formatted SFT
- 23.8K in-context learning formatted entries
- Teaches the model to process multi-image demonstrations
- Learning rate: 1e-5, 1 epoch
Phase 2 — Zero-shot SFT
- 48.7K zero-shot entries, LLM-augmented
- Strengthens single-plot understanding across all question types
- Learning rate: 5e-6, 1 epoch
Total training data: 72.5K entries
Input
Input Type(s): Image, Text
Input Format(s):
- Image: PNG, JPEG
- Text: String
Input Parameters:
- Image: Two-Dimensional (2D)
- Text: One-Dimensional (1D)
Other Properties Related to Input: Single-image or multi-image quantum calibration experiment plots with text prompts delivered through an OpenAI-compatible API. Suggested inference settings: temperature=0.2 and max_tokens=16384. Context length: 262,144 tokens.
Output
Output Type(s): Text
Output Format(s):
- Text: String
Output Parameters:
- Text: One-Dimensional (1D)
Other Properties Related to Output: Natural language technical analysis, experimental conclusions, significance assessments, fit quality evaluations, parameter extractions, and experiment success classifications.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA hardware and software frameworks, the model achieves faster inference times compared to CPU-only solutions.
Software Integration
Runtime Engine(s):
- Custom runtime, BF16 serving precision
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Blackwell (
sm100,B200/B300/GB200) - NVIDIA Hopper (
sm90,H100/H200/GH200)
Supported Operating System(s):
- Linux (
Ubuntu 22.04+) - Windows (
26200.8246)
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks before deployment.
Model Version(s)
v1.0.0
Training and Evaluation Datasets
Training
Data Modality
- Image
- Text
Training Data Size
72.5K total entries (Phase 1: 23.8K ICL-formatted entries; Phase 2: 48.7K zero-shot entries).
Data Collection Method by dataset
- Synthetic (LLM-augmented)
Labeling Method by dataset
- Synthetic
Properties: Synthetically generated quantum calibration experiment plots with paired analytical text.
Evaluation Dataset
Data Collection Method by dataset:
- Synthetic
Labeling Method by dataset:
- Synthetic
Properties: Curated quantum calibration experiments with ground-truth labels derived from simulation parameters.
Inference
Acceleration Engine: Modded version of Sglang, BF16 precision Test Hardware:
- 1x NVIDIA RTX 4080 (12GB)
- 2x NVIDIA A10 (24GB)
Citation
@misc{akbarli2026qcopm,
title = {A Unified Framework for Calibration, Decoding, and Resource-Aware Processing in Hybrid Quantum-Classical Machine Learning},
author = {T. Akbarli},
year = {2026},
}