mobiu-q 1.0.0

Soft Algebra Optimizer for Quantum & Complex Optimization

Mobiu-Q

Mobiu-Q is a next-generation optimizer built on Soft Algebra and Demeasurement theory, enabling stable and efficient optimization in quantum variational algorithms (VQE, QAOA).

✨ Features

• +43% improvement over Adam optimizer in VQE benchmarks
• Soft Algebra update rule for stable convergence
• SPSA Demeasurement for noisy hardware (95% fewer measurements)
• Adaptive Trust Ratio - automatically adjusts to noise levels

📦 Installation

pip install mobiu-q

🔑 Activation

Get your free license key at mobiu-q.com

mobiu-q activate YOUR_LICENSE_KEY

Or set environment variable:

export MOBIU_Q_LICENSE_KEY=your-key

🚀 Quick Start

Clean Simulations

import numpy as np
from mobiu_q import MobiuQCore, Demeasurement, get_energy_function

# Load a built-in problem
energy_fn = get_energy_function("h2_molecule")

# Initialize optimizer
opt = MobiuQCore(mode="standard")

# Random starting point
params = np.random.uniform(-np.pi, np.pi, 12)

# Optimization loop
for step in range(100):
    E = energy_fn(params)
    grad = Demeasurement.finite_difference(energy_fn, params)
    params = opt.step(params, grad, E)
    
    if step % 20 == 0:
        print(f"Step {step}: E = {E:.6f}")

# Always end the session!
opt.end()

Noisy Quantum Hardware

# For NISQ devices, use noisy mode + SPSA
opt = MobiuQCore(mode="noisy")

for step in range(100):
    # SPSA: Only 2 measurements per step!
    grad, E = Demeasurement.spsa(energy_fn, params)
    params = opt.step(params, grad, E)

opt.end()

📊 Built-in Problems

from mobiu_q import list_problems, get_energy_function, get_ground_state_energy

print(list_problems())
# ['h2_molecule', 'lih_molecule', 'transverse_ising', ...]

energy_fn = get_energy_function("lih_molecule")
E0 = get_ground_state_energy("lih_molecule")
print(f"Ground state energy: {E0}")

⚙️ API Reference

MobiuQCore

MobiuQCore(
    license_key=None,     # Your license key (or use env var)
    mode="standard",      # 'standard' or 'noisy'
    base_lr=None,         # Learning rate (auto-set by mode)
    offline_fallback=True # Fall back to Adam if API unavailable
)

Methods:

• step(params, gradient, energy) → Updated parameters
• end() → End session (important!)
• reset() → Reset for new optimization

Demeasurement

• Demeasurement.finite_difference(fn, params) - Accurate, 2N measurements
• Demeasurement.parameter_shift(fn, params) - Exact for quantum circuits
• Demeasurement.spsa(fn, params) - Noisy-resistant, only 2 measurements!

💰 Pricing

Tier	Runs/Month	Price
Free	20	$0
Pro	Unlimited	$29/month

A "run" is one complete optimization session (start → steps → end).

Sessions ended within 60 seconds with <5 steps don't count.

🔒 Security

Your optimization runs on Mobiu's secure cloud infrastructure. The core Soft Algebra logic never leaves our servers - your client only sends parameters and gradients.

📄 License

Proprietary - see LICENSE.md

© Mobiu Technologies, 2025