Initial commit to the repository, paper included in readme.

Authored by: @LiveBacteria (Tyler Poore)

Major functionality of the theory integration has been omitted as of this time. As such, later commits will contain the files as the theory is completed.

.gitignore

@@ -0,0 +1,20 @@
+node_modules
+.vscode
+vis/__pycache__
+vis/neural_mesh
+vis/octants
+vis/adjacencyMap.json
+vis/data.json
+vis/test.py
+vis/progress.txt
+vis/visualise.py_old
+trace32.exe
+scratch
+progress.txt
+adjacencyMap.json
+kdTree.js
+kdtreeTest.js
+nMesh.json_old
+pineconeTest.js
+cudaTest.js
+.env

QNN.js

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+class Neuron {
+  constructor() {
+    this.connections = [];
+    this.value = Math.random();
+    this.weight = Math.random();
+  }
+
+  connectTo(neuron) {
+    this.connections.push(neuron);
+  }
+}
+
+class Superposition {
+  constructor(neurons) {
+    this.neurons = neurons;
+    this.position = this.calculatePosition();
+  }
+
+  calculatePosition() {
+    let x = 0;
+    let y = 0;
+    let z = 0;
+
+    for (let neuron of this.neurons) {
+      x += neuron.x;
+      y += neuron.y;
+      z += neuron.z;
+    }
+
+    return {
+      x: x / this.neurons.length,
+      y: y / this.neurons.length,
+      z: z / this.neurons.length,
+    };
+  }
+}
+
+class NeuralNetwork {
+  constructor(size) {
+    this.size = size;
+    this.neurons = this.createNeurons();
+    this.superpositions = [];
+  }
+
+  createNeurons() {
+    let neurons = [];
+    for (let i = 0; i < this.size; i++) {
+      for (let j = 0; j < this.size; j++) {
+        for (let k = 0; k < this.size; k++) {
+          let neuron = new Neuron();
+          neuron.x = i;
+          neuron.y = j;
+          neuron.z = k;
+          neurons.push(neuron);
+        }
+      }
+    }
+    return neurons;
+  }
+
+  activateRandomNeurons(percentage) {
+    let numToActivate = Math.floor(this.neurons.length * percentage);
+    for (let i = 0; i < numToActivate; i++) {
+      let neuron =
+        this.neurons[Math.floor(Math.random() * this.neurons.length)];
+      neuron.value = 1;
+    }
+  }
+
+  createSuperposition() {
+    let activatedNe                                                                                                                                            V+9urons = this.neurons.filter((neuron) => neuron.value === 1);
+    let superposition = new Superposition(activatedNeurons);
+    this.superpositions.push(superposition);
+  }
+}
+
+let neuralNetwork = new NeuralNetwork(20);
+neuralNetwork.activateRandomNeurons(0.05);
+neuralNetwork.createSuperposition();

generateNeurons.js

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+const fs = require("fs");
+const path = require("path");
+const math = require("mathjs");
+
+class Neuron {
+  constructor(x, y, z) {
+    this.x = x;
+    this.y = y;
+    this.z = z;
+  }
+}
+
+process.on(
+  "message",
+  ({ centerX, centerY, centerZ, neuronsPerProcess, filename }) => {
+    // Create the directory if it doesn't exist
+    let dir = path.dirname(filename);
+    if (!fs.existsSync(dir)) {
+      fs.mkdirSync(dir, { recursive: true });
+    }
+
+    let writeStream = fs.createWriteStream(filename, { flags: "a" });
+
+    for (let j = 0; j < neuronsPerProcess; j++) {
+      let x = centerX + math.random(-0.1, 0.1);
+      let y = centerY + math.random(-0.1, 0.1);
+      let z = centerZ + math.random(-0.1, 0.1);
+
+      let neuron = new Neuron(x, y, z);
+      writeStream.write(JSON.stringify(neuron) + ",\n");
+    }
+
+    writeStream.end();
+  }
+);