mobiu-q 1.0.7

Soft Algebra Optimizer for Quantum & Complex Optimization

Mobiu-Q

The VQE Optimizer Built for Quantum Noise.

Mobiu-Q uses Soft Algebra to achieve 62% better convergence than Adam on VQE molecular simulations. Validated on IBM Quantum hardware.

🎯 What is Mobiu-Q?

Mobiu-Q is a VQE-optimized optimizer that prevents variational collapse on noisy quantum hardware. While Adam and other classical optimizers chase noise into non-physical solutions, Mobiu-Q's Trust Ratio mechanism knows when to stop.

Why VQE?

Mobiu-Q is specifically designed and validated for Variational Quantum Eigensolver (VQE) problems:

• Molecular ground state estimation
• Quantum chemistry simulations
• Material science calculations

For VQE, Mobiu-Q provides significant advantages over Adam. Other quantum algorithms (like QAOA) may not see the same benefits.

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🚀 Quick Start

pip install mobiu-q

from mobiu_q import MobiuQCore, Demeasurement

# Initialize optimizer
opt = MobiuQCore(license_key="your-key", mode="standard")

# VQE optimization loop
for step in range(100):
    energy = energy_fn(params)
    grad = Demeasurement.finite_difference(energy_fn, params)
    params = opt.step(params, grad, energy)

opt.end()

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📊 Results

IBM Hardware Validation (Dec 2025)

Optimizer	H₂ Final Energy	Ground State (-1.174 Ha)
Adam	-1.681 Ha	❌ FAILED (crashed into noise)
Mobiu-Q	-1.176 Ha	✅ SUCCESS (gap: 0.002 Ha)

Simulation Benchmarks

Problem	Mobiu-Q vs Adam
H₂ VQE	+62% better convergence
LiH VQE	+45% better convergence
VQE + Shot Noise	+9% better convergence

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⚙️ Modes

Mode	Learning Rate	Best For
standard	0.01	Clean simulations, statevector
noisy	0.02	Real hardware (IBM, IonQ, Rigetti)

# For simulators
opt = MobiuQCore(license_key="xxx", mode="standard")

# For real quantum hardware
opt = MobiuQCore(license_key="xxx", mode="noisy")

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🔬 Gradient Estimation

from mobiu_q import Demeasurement

# For clean simulations (2*N function calls)
grad = Demeasurement.finite_difference(energy_fn, params)

# For noisy environments (only 2 function calls!)
grad, energy = Demeasurement.spsa(energy_fn, params, c_shift=0.1)

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🔄 Multi-Seed Experiments

opt = MobiuQCore(license_key="your-key", mode="standard")

for seed in range(40):
    np.random.seed(seed)
    params = initialize_params()
    
    for step in range(100):
        energy = energy_fn(params)
        grad = Demeasurement.finite_difference(energy_fn, params)
        params = opt.step(params, grad, energy)
    
    results.append(energy_fn(params))
    opt.new_run()  # Reset state for next seed

opt.end()  # Close session

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🔥 Full Example: IBM Hardware VQE

from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2
from mobiu_q import MobiuQCore, Demeasurement

# Connect to IBM Quantum
service = QiskitRuntimeService(channel="ibm_quantum")
backend = service.least_busy(simulator=False, min_num_qubits=5)
estimator = EstimatorV2(mode=backend)

# Initialize Mobiu-Q for noisy hardware
opt = MobiuQCore(license_key="your-key", mode="noisy")

# VQE optimization
for step in range(60):
    grad, energy = Demeasurement.spsa(
        lambda p: estimator.run([(circuit, observable)]).result()[0].data.evs.item(),
        params,
        c_shift=0.12
    )
    params = opt.step(params, grad, energy)

opt.end()

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📚 Built-in VQE Problems

from mobiu_q import list_problems, get_energy_function, get_ground_state_energy

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

# Get H2 molecule VQE
energy_fn = get_energy_function("h2_molecule")
E0 = get_ground_state_energy("h2_molecule")

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💳 Pricing

Plan	Price	Runs
Free	$0	5 VQE runs/month
Pro	$19/month	Unlimited

Get your license at app.mobiu.ai

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⚠️ Scope

Mobiu-Q is optimized for VQE (Variational Quantum Eigensolver) problems.

Other quantum algorithms like QAOA may not see significant improvements over Adam. We are actively researching extensions to other domains.

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📖 Learn More

• Website
• Hardware Validation Report
• PyPI Package

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