QFVCS: Quantum Fractal Visualization & Computation System

Quantum-Neural Integration Framework

This repository presents the public branch of the Quantum Fractal Visualization & Computation System, designed to bridge quantum physics simulation with neural network optimization through Schrödinger-like evolution of network parameters.

Core Integration Concept

QFVCS leverages its quantum wave function simulator to explore a novel approach to neural network optimization:

Quantum Simulation Neural Network ┌──────────────────┐ ┌──────────────┐ Wave Function <───>│ Schrödinger Eqn. │<───────────>│ BitNet b1.58 │ Evolution └──────────────────┘ Mapping └──────────────┘

System Architecture

QFVCS combines advanced quantum simulation capabilities with extensible visualization frameworks:

┌─────────────────────────────────────────────────────┐ │ QFVCS Architecture │ ├─────────────────┬───────────────┬──────────────────┤ │ Quantum Engine │ Fractal Math │ Visualization │ │ - Schrödinger │ - Zeta │ - 2D/3D/ND │ │ Evolution │ Functions │ Renderers │ │ - Hamiltonian │ - Complex │ - Isosurface │ │ Construction │ Mappings │ Generation │ │ - Wave Function │ - Dimensional │ - Particle │ │ Dynamics │ Transforms │ Tracking │ └─────────────────┴───────────────┴──────────────────┘

Quantum-Neural Mapping

The system enables mapping between BitNet's ternary weights and quantum states:

In this mapping, the BitNet ternary weights are encoded as discrete quantum states, with the amplitudes evolving under quantum rules. This allows the exploration of neural optimization as a physical process rather than an abstract gradient calculation.

Key Mathematical Framework

The core principle is representing neural network weights as quantum states:

Wave Function Equation

These states evolve according to the Schrödinger equation:

Schrödinger Equation

Where H is a Hamiltonian designed to optimize network performance:

This formalism opens the door to interpreting training as physical wave dynamics over parameter space, including effects such as superposition, interference, and tunneling between optima.

Hardware and Software Collaboration Framework

Contribution Areas

  1. BitNet integration optimized for dynamic quantum simulation
  2. Hardware acceleration modules for ternary weight processing
  3. Hamiltonian formulation techniques for network-level learning
  4. Visualization tools for interpreting quantum-neural interactions
  5. Benchmarks comparing quantum-driven vs. classical optimization

References