KPU 1.1.2
KPU Quantum Computing Chip - AI & Cybersecurity Innovation
Requires Python: >=3.7



# KPU Quantum Chip  
**Software-Based Quantum Processing Unit for High-Performance Quantum Computations**  



## Overview

The **KPU Quantum Chip** is a cutting-edge, software-based quantum processing unit that leverages quantum-inspired formulasâ€”such as Kala Time Travel and Quantum Multiverse operationsâ€”along with advanced simulations like Quantum Fourier Transforms, quantum superposition, relativistic proton speed, and tensor space-time curvature computations. Designed to run on multiple devices (CPU, GPU, or iCPU), the KPU Chip is engineered to push the boundaries of performance, aiming to achieve near light-speed computation.

## Features

- **Dynamic Device Selection:** Automatically detects and uses GPU if available, otherwise defaults to CPU.
- **Quantum Calculations:** Implements multiple quantum formulas:
  - **Kala Time Travel Formula:** Simulates time inversion with nonlinear modulation.
  - **Quantum Multiverse Formula:** Applies exponential decay and cosine modulation.
  - **Quantum Fourier Transform:** Uses FFT to process quantum signals.
  - **Quantum Superposition & Relativistic Proton Speed:** Simulate quantum state operations and near-light-speed computations.
  - **Tensor Space-Time Curvature:** Computes a curvature metric using the determinant of a square matrix derived from the tensor.
- **Modular Architecture:** Configurable number of QuantumKalaCell cores through a simple configuration system.
- **Rich Table Summary:** Uses the [Rich](https://github.com/Textualize/rich) library to display an attractive chip summary.
- **Benchmarking Tools:** Measure performance of heavy calculations, including proton speed optimizations.
- **Extensibility:** Ready to integrate into larger projects such as hash crackers, advanced quantum simulations, AI-driven cryptography, and more.


## Usage

### **Running the KPU Chip Simulation**

Below is an example that demonstrates how to initialize the KPU Quantum Chip, print its architecture summary, and run a performance benchmark using a random input tensor:

```python
import tensorflow as tf
import numpy as np
import time
from Kalacell import KPUChip, KPUConfig, optimize_proton_speed, compute_riemann_zeta, compute_black_hole_entropy, compute_quantum_finance_risk, compute_climate_change_impact, kpu_banner

# Display a cool banner for the chip
kpu_banner()

# Create a KPU configuration and instantiate the chip
config = KPUConfig(vocab_size=512, embed_dim=256, num_cores=32, output_units=20)
chip = KPUChip(config)

# Print the chip summary (using Rich tables)
chip.print_chip_summary()

# Generate a sample input tensor (simulate global parameters)
test_input = tf.random.uniform((1, 100), minval=0, maxval=config.vocab_size, dtype=tf.int32)

# Benchmark the chip performance
start_time = time.time()
output = chip(test_input)
end_time = time.time()

print("\nKPUChip Output Shape:", output.shape)
print("KPUChip Output:", output.numpy())
print(f"Quantum Computation Speed: {end_time - start_time:.6f} seconds")
```

### **Advanced Quantum Calculations**

You can also run advanced quantum computations like:

- **Riemann Zeta Function Approximation:**

  ```python
  result = compute_riemann_zeta(s=2.0, n_terms=1000000)
  print(f"Riemann Zeta(2): {result}")
  ```

- **Black Hole Entropy Calculation:**

  ```python
  entropy = compute_black_hole_entropy(mass=1.989e30)  # 1 solar mass in kg
  print(f"Black Hole Entropy: {entropy} bits")
  ```

- **Quantum Finance Risk Analysis:**

  ```python
  risk = compute_quantum_finance_risk(factor=1.0)
  print(f"Quantum Finance Risk: {risk}")
  ```

- **Climate Change Impact Estimation:**

  ```python
  impact = compute_climate_change_impact(co2=400)
  print(f"Climate Change Impact (CO2=400ppm): {impact}")
  ```

## Performance Benchmark

For a large-scale computation like summing 1,000,000 numbers, using NumPy's vectorized operations (or optimized TensorFlow operations) ensures near-light-speed performance.

```python
start = time.perf_counter()
result = np.sum(np.arange(1, 1_000_001))
end = time.perf_counter()

print("Addition result:", result)
print("Total execution time:", end - start, "seconds")
```

## Contributing

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

## License

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.