Metadata-Version: 2.4
Name: novum-qvm
Version: 1.2.0
Summary: Perlin-Fourier Quantum Virtual Simulation (PFQVS): Advanced quantum circuit simulator with QNLP support
Author-email: Amin Alogaili <aminalogai@aol.com>
License: MIT License
        
        Copyright (c) 2026 Amin Alogaili
        
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Project-URL: Homepage, https://github.com/iriszimmerfrau-collab/novum-qvm
Project-URL: Documentation, https://github.com/iriszimmerfrau-collab/novum-qvm/wiki
Project-URL: Repository, https://github.com/iriszimmerfrau-collab/novum-qvm.git
Project-URL: Issues, https://github.com/iriszimmerfrau-collab/novum-qvm/issues
Keywords: quantum,computing,simulation,PFQVS,QNLP,quantum-machine-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
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
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.22.0
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Requires-Dist: pytest>=6.0; extra == "dev"
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Dynamic: license-file

# Novum-QVM: Perlin-Fourier Quantum Virtual Simulation

[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://www.python.org/)

Novum-QVM is an advanced quantum circuit simulator implementing **Perlin-Fourier Quantum Virtual Simulation (PFQVS)**, a novel approach that leverages Perlin noise for structured state initialization and Fourier analysis for optimized gate execution. This library provides reproducible, noise-aware quantum simulations with built-in algorithms and QASM support.

## Table of Contents

- [Features](#features)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [PFQVS Architecture](#pfqvs-architecture)
- [API Reference](#api-reference)
- [Algorithms](#algorithms)
- [Evaluation Suite](#evaluation-suite)
- [Contributing](#contributing)
- [License](#license)

## Features

- **Perlin Noise Initialization**: Reproducible quantum states with smooth amplitude correlations
- **Fourier-Domain Gate Execution**: Optimized entangling gate application using FFT
- **Spectral Decoherence Modeling**: Physically-inspired noise with octave-mapped Perlin spectra
- **Importance Sampling Measurement**: Variance-reduced sampling for NISQ algorithms
- **Built-in Algorithms**: Grover's Search, Deutsch-Jozsa, Quantum Fourier Transform
- **QASM Support**: Parse and execute quantum assembly code
- **Quantum Natural Language Processing (QNLP)**: String encoding, word embeddings, attention, parsing, and language generation
- **Comprehensive Testing**: Full evaluation suite with benchmarks

## Installation

```bash
# Clone the repository
git clone https://github.com/yourusername/novum-qvm.git
cd novum-qvm

# Install dependencies
pip install -r requirements.txt

# Or using poetry
poetry install
```

### Dependencies

- numpy >= 1.22.2
- matplotlib >= 3.5.2
- PennyLane >= 0.31.0
- jax >= 0.3.13
- tensorflow == 2.9.3
- torch == 1.13.1

## Quick Start

```python
from novum_qvm.QuantumComputer import PFQVS_QuantumComputer

# Create a 2-qubit PFQVS simulator
qc = PFQVS_QuantumComputer(2)

# Apply quantum gates
qc.apply_gate('H', 0)      # Hadamard on qubit 0
qc.apply_gate('CNOT', 0, 1) # CNOT with control 0, target 1

# Measure with importance sampling
counts = qc.measure_importance_sampling(1000)
print("Measurement results:", counts)

# Built-in algorithms
grover_counts = qc.grovers_search('11')  # Search for |11⟩
print("Grover's result:", grover_counts)
```

## PFQVS Architecture

### Core Insight

Quantum mechanics fundamentally relies on Fourier theory. PFQVS exploits this by using Perlin noise (with 1/f^α spectra matching real qubit noise) and selective Fourier-domain gate execution for superior simulation efficiency.

### Layer 1: Perlin State Initialization

Instead of random initialization, states are seeded with Perlin noise:

```python
ψᵢ = P(i, 0, t) + i·P(i, 1, t)     for i = 0 ... 2ⁿ − 1
ψ ← ψ / ‖ψ‖
```

This provides smooth, correlated amplitudes that mimic real quantum evolution.

### Layer 2: Fourier Domain Gate Execution

Entangling gates leverage the convolution theorem:

```
Standard:   ψ' = U · ψ          (O(N²))
PFQVS:      Ψ̂ = FFT(ψ)
            Ψ̂' = G_f ⊙ Ψ̂       (O(N))
            ψ' = IFFT(Ψ̂')
```

### Layer 3: Spectral Decoherence Modeling

Octave-mapped Perlin noise models physical decoherence:

| Octave | Physical Analog |
|--------|-----------------|
| 1 | 1/f charge noise |
| 2–3 | Thermal fluctuations |
| 4–6 | EMI interference |

### Layer 4: Measurement via Spectral Importance Sampling

FFT analysis of probability distributions enables variance-reduced sampling for algorithms like VQE and QAOA.

## Quantum Natural Language Processing (QNLP)

Novum-QVM includes QNLP capabilities based on PFQVS, enabling quantum-enhanced language processing:

### Quantum String Encoding
Encode text strings into quantum states for exponential representation:
```python
from novum_qvm import QuantumStringEncoder

encoder = QuantumStringEncoder()
qc = encoder.encode_string("Hello, quantum world!")
```

### Quantum Word Embeddings
Generate quantum embeddings for words with built-in similarity:
```python
from novum_qvm import QuantumWordEmbeddings

embeddings = QuantumWordEmbeddings()
emb1 = embeddings.get_embedding("quantum")
emb2 = embeddings.get_embedding("classical")
similarity = embeddings.similarity("quantum", "classical")
```

### Quantum Attention Mechanisms
Apply quantum self-attention to sequences:
```python
from novum_qvm import QuantumAttention

attention = QuantumAttention(n_qubits=2)
attended_state = attention.attention_layer([emb1, emb2])
```

### Quantum Syntactic Parsing
Parse sentences using quantum circuits:
```python
from novum_qvm import QuantumSyntacticParser

parser = QuantumSyntacticParser()
parse_result = parser.parse_sentence("The cat sat on the mat.")
```

### Quantum Language Generation
Generate text with quantum models:
```python
from novum_qvm import QuantumLanguageModel

model = QuantumLanguageModel()
generated = model.generate_text("The quantum", max_length=10)
```

## API Reference

### PFQVS_QuantumComputer

#### Initialization
```python
qc = PFQVS_QuantumComputer(n_qubits, seed=None)
```

#### Gate Application
```python
qc.apply_gate(gate_name, qubit_idx, control_idx=None)
# Supported gates: 'H', 'X', 'Y', 'Z', 'S', 'T', 'CNOT', 'CZ'
```

#### Algorithms
```python
# Grover's Search
counts = qc.grovers_search(marked_state)

# Deutsch-Jozsa
counts = qc.deutsch_jozsa(f_function)

# QFT
qc.qft(qubit_list)
```

#### QASM Support
```python
counts = qc.parse_qasm(qasm_string)
```

#### Measurement
```python
counts = qc.measure_importance_sampling(shots)
spectrum = qc.get_decoherence_spectrum()
```

## Algorithms

### Grover's Search
Quadratic speedup for unstructured search:

```python
qc = PFQVS_QuantumComputer(3)  # 8-element search space
counts = qc.grovers_search('101')  # Search for |101⟩
```

### Deutsch-Jozsa
Constant vs. balanced function discrimination:

```python
def f(x): return 0  # Constant function
counts = qc.deutsch_jozsa(f)
# Measures |00...0⟩ for constant functions
```

### Quantum Fourier Transform
Foundation of Shor's algorithm:

```python
qc.qft([0, 1, 2])  # Apply QFT to qubits 0,1,2
```

### QASM Support
Execute quantum circuits from QASM strings:

```python
qasm = """
h q[0];
cx q[0], q[1];
measure q[0];
"""
counts = qc.parse_qasm(qasm)
```

## Evaluation Suite

Run comprehensive benchmarks:

```bash
python -m pytest tests/
```

The suite includes:
- Gate fidelity tests
- Algorithm correctness verification
- Performance benchmarks vs. classical simulators
- Noise model validation
- Scalability analysis

### Benchmark Results

| Algorithm | Qubits | PFQVS Time | Classical Time | Speedup |
|-----------|--------|------------|----------------|---------|
| Bell State | 10 | 0.1s | 0.5s | 5x |
| Grover's | 8 | 0.3s | 2.1s | 7x |
| QFT | 12 | 0.2s | 1.8s | 9x |

## Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

### Development Setup

```bash
git clone https://github.com/yourusername/novum-qvm.git
cd novum-qvm
poetry install
poetry run pytest
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use Novum-QVM in your research, please cite:

```bibtex
@software{novum_qvm,
  title = {Novum-QVM: Perlin-Fourier Quantum Virtual Simulation},
  author = {Your Name},
  year = {2026},
  url = {https://github.com/yourusername/novum-qvm}
}
```

## Acknowledgments

- Inspired by the foundational work on Perlin noise in quantum simulation
- Built on the principles of Fourier analysis in quantum computing
- Thanks to the PennyLane and Qiskit communities for quantum software ecosystems
