Metadata-Version: 2.4
Name: mobiu-q
Version: 1.8.7
Summary: Soft Algebra Optimizer for Quantum & Complex Optimization
Author-email: Mobiu Technologies <ai@mobiu.ai>
License: Proprietary
Project-URL: Homepage, https://app.mobiu.ai
Project-URL: Documentation, https://pypi.org/project/mobiu-q/
Keywords: quantum,optimization,VQE,QAOA,machine-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.21.0
Requires-Dist: requests>=2.25.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Provides-Extra: full
Requires-Dist: scipy>=1.7.0; extra == "full"
Requires-Dist: qiskit>=0.40.0; extra == "full"

# Mobiu-Q (v1.8.7)

**Hybrid Soft-Algebra Optimizer for Quantum Computing**

[![PyPI version](https://badge.fury.io/py/mobiu-q.svg)](https://badge.fury.io/py/mobiu-q)
[![Win Rate](https://img.shields.io/badge/Win%20Rate-99.3%25-brightgreen)](https://mobiu.ai)
[![License](https://img.shields.io/badge/License-Proprietary-blue)](https://mobiu.ai)

Mobiu-Q is a cloud-based optimizer that uses **Soft Algebra** to accelerate quantum algorithms. It demonstrates **algorithmic superiority** in standard conditions and **extreme resilience** in noisy environments, achieving a 99.3% win rate across 1,000 benchmarks.

---

## 🚀 The Problem
* **In Simulation (Standard):** Standard optimizers (Adam) often overshoot the minimum or converge slowly due to rigid momentum.
* **On Hardware (Noisy):** Shot noise causes gradients to fluctuate, trapping optimizers in false local minima.

## 💡 The Solution: Hybrid Cross-Coupling
Mobiu-Q wraps your optimization loop with a cloud-based brain that cross-validates every step using **Soft Algebra Logic**:

```math
$$S_{t+1} = (\gamma \cdot S_t) \cdot \Delta_t + \Delta_t$$
````

Where $\Delta_t$ represents the dual signal $(a_t, b_t)$. This allows the "Cloud Brain" to adjust the learning rate dynamically based on algebraic trust rather than just historical averages.

** Note: In v1.6+, the optimizer utilizes a decoupled Vector EMA implementation of the Soft Algebra state evolution to maximize numerical stability on noisy hardware.

-----

## 📊 Comprehensive Benchmarks

### 1\. Robustness & Generalization (1,000 Runs)

**Settings:** Standard Mode, LR=0.01 (Fair fight vs Adam default).
Tested across 10 different Hamiltonians, 100 seeds each.

| Problem Domain | Improvement vs Adam | Win Rate |
|----------------|---------------------|----------|
| **H2 Molecule** | **+49.12%** | 100/100 |
| **LiH Molecule** | **+44.87%** | 100/100 |
| **Transverse Ising** | **+22.25%** | 100/100 |
| **Heisenberg XXZ** | **+23.01%** | 99/100 |
| **MaxCut** | **+40.50%** | 54/60 |
| **Vertex Cover** | **+39.59%** | 54/60 |
| **Max Independent Set** | **+28.85%** | 50/60 |
| **TOTAL AVERAGE** | **+35.45%** | **557/580** |

*\> **Insight:** Even in standard conditions, Mobiu-Q finds deeper energy levels than Adam.*

### 2\. The "Ultimate Fair" Test (High Noise)

**Settings:** Noisy Mode, 500 Shots, CRN (Common Random Numbers).
We compared Mobiu-Q against Adam and Naive EMA under heavy quantum noise.

![Mobiu-Q Benchmark](https://mobiu.ai/wp-content/uploads/2025/12/ultimate.png?raw=true)

*Figure 1: Green (Mobiu-Q) ignores the noise floor that traps Adam (Blue) and fails Naive EMA (Orange).*

-----

## 📦 Installation

```bash
pip install mobiu-q
```

-----

## ⚡ Quick Start

### 1\. VQE (Chemistry)

Best for molecular simulations (`H2`, `LiH`, etc).

```python
from mobiu_q import MobiuQCore
import numpy as np

# Initialize Cloud Optimizer
opt = MobiuQCore(
    license_key="YOUR-LICENSE-KEY",
    problem="vqe",        # Optimized for chemistry
    mode="standard",      # Use "noisy" for real hardware
    base_lr=0.01          # Standard learning rate
)

# Your Physics Loop
params = np.random.uniform(-0.1, 0.1, n_params)

for step in range(80):
    # 1. Measure (Local)
    energy = measure_energy(params)
    gradient = calculate_gradient(params)
    
    # 2. Optimize (Cloud Brain)
    params = opt.step(params, gradient, energy)

opt.end()
```

### 2\. QAOA (Combinatorial)

Best for MaxCut, Vertex Cover, and rugged landscapes.

```python
opt = MobiuQCore(
    license_key="YOUR-LICENSE-KEY",
    problem="qaoa",       # Uses Super-Equation Δ†
    mode="noisy",         # Recommended for QAOA
    base_lr=0.1           # Aggressive learning rate
)
```

-----

## 🔑 Pricing & Licenses

We offer a free tier for researchers and students.

  * **Free:** 5 runs / month (No credit card required).
  * **Pro:** Unlimited runs, priority support.

**[Get your License Key Here](https://app.mobiu.ai)**

-----

## ❓ FAQ

**Q: Why use cloud optimization?**
A: The Soft Algebra computation requires stateful cross-coupling history that is best managed centrally. It allows us to deploy updates to the "Brain" without you needing to update your Python package.

**Q: Is my data safe?**
A: We only receive anonymous gradients and energy scalars. Your Hamiltonian / Circuit structure remains locally on your machine. We never see your IP.

-----

## Support

  * **Documentation:** [pypi.org/project/mobiu-q](https://pypi.org/project/mobiu-q/)
  * **Email:** ai@mobiu.ai

-----

*Proprietary technology. All rights reserved by Mobiu Technologies.*
