Self-Emergent Processor: Thesis Work
A comprehensive exploration of recursive computational frameworks, quantum-inspired algorithms, and the emergence of coherence in complex systems.
Overview
The Self-Emergent Processor (SEP) represents a novel computational framework that bridges discrete computation, quantum mechanics, and number theory. This body of work explores how identity, complexity, and meaning can arise naturally from prime-number-gated state transitions.
Recursive Prime-Gated Framework
A Concise Technical Overview
This adjusted thesis presents the core SEP framework with emphasis on practical implementation and empirical validation. It demonstrates through simulations and C++ implementation how SEP reliably detects structure in data streams.
- Prime-gated recursion for emergence
- Coherence metrics and stability analysis
- Validated proofs of concept
- Financial time-series applications
Computational Framework for Emergent Coherence
Comprehensive Theoretical Development
A full academic treatment integrating perspectives from quantum complexity theory, number theory, and philosophy. This version includes extensive citations and positions SEP within the broader scientific context.
- Theoretical foundations from physics and mathematics
- Quantum mechanical analogies and mappings
- Connections to Riemann Hypothesis
- Entropy and emergent meaning
Self-Emergent Information Processing
A Formal Mathematical Treatment
This paper presents SEP as a recursive, information-theoretic system with rigorous mathematical formalism. It redefines fundamental physical concepts through the lens of phase alignment and information coherence.
- Prime-gated iteration as discrete time
- Discrete Lagrangian formulation
- Information as gravitational coherence
- SEP Engine architecture and algorithms
Key Concepts
Prime-Gated Recursion
System evolution driven by prime numbers as fundamental time steps, ensuring non-periodic dynamics.
Coherence Metrics
Quantifiable measures of pattern self-similarity and internal consistency in data streams.
Quantum-Inspired Algorithms
QBSA (Quantum Bit State Analysis) and QFH (Quantum Fourier Hierarchy) for pattern detection.
Emergent Complexity
How simple recursive rules generate complex, coherent structures from apparent randomness.
Implementation
The SEP Engine is a high-performance C++ implementation with CUDA acceleration, demonstrating that these theoretical principles can be operationalized for real-world data analysis. The engine processes arbitrary data streams to extract emergent patterns and quantify coherence.
Data-Agnostic Processing
Analyzes any data type as raw byte streams without format-specific parsing.
GPU Acceleration
CUDA kernels for real-time processing of high-volume data streams.
Proven Performance
Benchmarked at 280MB processed in ~2 minutes on GPU hardware.