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:
- Neural Weights: BitNet's 1.58-bit precision weights using {-1, 0, +1} values
- Quantum States: Represented as qutrits in the wave function simulation
- Evolution Rules: Schrödinger equation governs weight optimization
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:
These states evolve according to the Schrödinger equation:
Where H is a Hamiltonian designed to optimize network performance:
- Loss function contributes to potential terms
- Layer connectivity creates coupling terms
- Training signals (like DPO) act as external fields
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
- The implementation of BitNet b1.58 is a core development focus within QFVCS. We welcome contributors with expertise in ultra-low-bit quantization and ternary network architectures.
- Current integration targets CPU-based inference, with an open roadmap for community-driven GPU and NPU support.
- QFVCS provides the quantum simulation backbone, enabling seamless experimentation with Schrödinger-governed BitNet dynamics.
- We are actively forming collaborations to prototype and benchmark novel hardware acceleration strategies tailored for ternary neural systems.
- Join our developer community to co-design the intersection of quantum simulation and energy-efficient neural computation.
Contribution Areas
- BitNet integration optimized for dynamic quantum simulation
- Hardware acceleration modules for ternary weight processing
- Hamiltonian formulation techniques for network-level learning
- Visualization tools for interpreting quantum-neural interactions
- Benchmarks comparing quantum-driven vs. classical optimization
References
