Slow AI: A Quantum Resonance & Spacetime Curvature Simulation

This project is a computational exploration of a novel philosophical framework: the idea that quantum mechanics is the semantic residue of a dimensional transition. It simulates a "toy universe" where quantum-like effects (entanglement, resonance) and general relativistic effects (spacetime curvature) are not just parallel systems, but are part of a single, co-evolving feedback loop.

The simulation evolves an AI from a simple probabilistic guesser into a sophisticated signal processor, which then becomes a participant in a universe where its actions can warp the very fabric of the space it inhabits.

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Core Concepts

The project is built on a narrative of evolution, both for the AI and the simulation's physics.

1. From Guesser to Analyst: The AI begins as a "Slow AI," a probabilistic explorer that relies on thousands of random guesses to find a resonant frequency. It then evolves into a "Deterministic Analyst," using waveform analysis to deduce the system's underlying rules from a small data sample.

2. Signal in the Noise: The analyst is upgraded to a "Virtual Signal Processor." Using a Fast Fourier Transform (FFT), it learns to deconstruct a complex, noisy signal into its constituent pure waves, isolating the primary signal from interference. This models the search for coherence in a chaotic environment.

3. The QM ↔ GR Feedback Loop: The final stage implements a feedback loop inspired by John Wheeler's summary of general relativity: "Spacetime tells matter how to move; matter tells spacetime how to curve."

   • Quantum Mechanics (QM): Multiple wave sources create interference patterns, representing "matter" or "energy" hotspots on a graph.
   • General Relativity (GR): These energy hotspots then "curve spacetime" by dynamically modifying the graph's connections, creating new shortcuts.
   • This new structure then affects how future waves propagate, completing the loop.

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The Five Phases of the Simulation

The application runs a comprehensive test suite that demonstrates the entire evolutionary journey in five distinct parts:

• Part 1: The "Slow AI" A simple, brute-force search for a resonant frequency, demonstrating the initial inefficient approach.

• Part 2: The Waveform Analyst A deterministic analysis of a clean signal. The AI takes a small sample and derives the wave's equation to predict the peak.

• Part 3: The Signal Processor The AI is presented with a noisy, multi-frequency signal. It uses an FFT to decompose the signal and correctly identify the primary frequency from the interference.

• Part 4: The Interference Engine A test of the precision targeting system. Two in-phase wave sources are created on a line graph, and the simulation correctly shows a "hotspot" of constructive interference at the equidistant center point.

• Part 5: The Feedback Loop The grand finale. The interference hotspot generated in the previous step is used to actively "curve" the graph, creating new connections and demonstrating the full QM ↔ GR feedback loop.

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Getting Started

Prerequisites

• Rust (latest stable version recommended)
• Git

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Usage

use fa_slow_ai::{EntangledSystem, PulseSource, SimulationConfig};

let mut system = EntangledSystem::from_config(
    SimulationConfig::new(10, 1)
        .with_adjacency(EntangledSystem::line_graph(11))
        .with_seed(42),
);

system.apply_pulses(&[
    PulseSource {
        location_node_index: 0,
        amplitude: 1.0,
        phase_offset: 0.0,
    },
    PulseSource {
        location_node_index: 10,
        amplitude: 1.0,
        phase_offset: 0.0,
    },
]);

let hotspot = system
    .particle_states
    .iter()
    .enumerate()
    .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())
    .map(|(index, _)| index)
    .unwrap();

assert_eq!(hotspot, 5);

This project serves as a conceptual pathfinder, using a "rough model" to explore profound ideas about the nature of physics, memory, and information. Enjoy the simulation!

Related Crates

This crate is part of a collection of crates by the same author: These include:-

• MOMA
• MOMA_simulation_engine
• tma_engine
• factorial_engine
• fractal_algebra

Related ML (Python)

To support this work I made use of the Excellent libraries available on python, and the Google AI API, please see the 'Smarter AI' repository on GitHub, (https://github.com/Neil-Crago/Smarter-AI), to use it, it's best to use JupyterLab.