Quantum Neural Network Framework (QNN-F)

A Foundational Quantum-Native Machine Learning Framework

PROPRIETARY SOFTWARE - ALL RIGHTS RESERVED

This software is NOT open source. Redistribution, publication, and derivative works are prohibited without explicit written permission. See LICENSE for details.

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Abstract

This framework provides a complete quantum-native redesign of deep learning architectures. Unlike approaches that merely "quantize" classical networks, QNN-F builds architectures that exploit fundamental quantum mechanical principles:

• Superposition: Process 2^n states simultaneously with n qubits
• Entanglement: Encode correlations without O(n^2) learnable parameters
• Interference: Non-linearity emerges from quantum measurement
• GPU Acceleration: Native support for NVIDIA cuQuantum via lightning.gpu

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Key Features

QAFE (Quantum Adaptive Feature Encoding)

Input-dependent quantum circuits that adapt topology based on data complexity.

from quantum_nn.qafe import QAFE

qafe = QAFE(input_dim=512, n_qubits=8, min_layers=1, max_layers=4)
output, meta = qafe(input_data)

Quantum Chemistry (VQE)

Inspired by Google's Willow chip breakthroughs:

from quantum_nn.chemistry import VQE, H2

molecule = H2(bond_length=0.74)
vqe = VQE(molecule.hamiltonian, n_qubits=4, ansatz='hardware_efficient')
energy = vqe.run(max_iterations=100)
print(f"Ground state energy: {energy:.6f} Hartree")

GPU-Accelerated Quantum Vision Transformer

from quantum_nn.architectures import GPUQuantumViT

config = {
    'image_size': 64, 'patch_size': 8, 'embed_dim': 64,
    'num_heads': 4, 'num_layers': 2, 'n_qubits': 4
}
model = GPUQuantumViT(config)

Universal Data Adapters

Works with any data type automatically:

from quantum_nn.data import UniversalDataLoader, DataConfig

config = DataConfig(data_path='./my_dataset', task='classification', n_qubits=8)
loader = UniversalDataLoader(config)
train, val, test = loader.get_loaders()

Data Type	Formats	Features
Images	PNG, JPG, TIFF	Auto-resize, augmentation
Tabular	CSV, Excel, Parquet	Missing values, scaling
Time-Series	CSV, TS	Sliding window, FFT encoding
Text	CSV, JSON	Tokenization, TF-IDF

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Architecture

quantum_nn/
├── qafe/           # Quantum Adaptive Feature Encoding
├── architectures/  # Q-ViT, Q-Transformer
├── chemistry/      # VQE, UCCSD, Molecules
├── data/           # Universal data adapters
├── core/           # Quantum circuits & layers
└── framework/      # Training utilities

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Installation

git clone https://github.com/yourusername/Quantum_NN.git
cd Quantum_NN
python -m venv .venv
.venv\Scripts\activate
pip install -e .

# For GPU acceleration
pip install pennylane-lightning[gpu]

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GPU Acceleration

Automatic backend detection:

Backend	Hardware	Speed
lightning.gpu	NVIDIA GPU	Fastest
lightning.qubit	CPU optimized	Fast
default.qubit	CPU fallback	Standard

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License

PROPRIETARY SOFTWARE LICENSE

This software is NOT open source. All rights reserved.

Prohibited:

• Publishing or redistributing
• Creating derivative works
• Commercial use without permission
• Filing patents based on these algorithms

See LICENSE for complete terms.

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Copyright 2024-2026 Quantum_NN Authors. All Rights Reserved.