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🧠 The Heart of QTC

Dive into the core of QTC - where we're making AI that plays nice with any hardware. It's all about polar valence, resilience, and balance. Sounds complex? Don't worry, we'll break it down!

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QTC Qubit-Tensor-Chain

Algorithms: Hardware Agnostic AI

Formats: Error-Proof AI Components

Models: Chiral Neural Networks

Fusion: Hybrid Quantum-Classical Integration

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Finance: AI-Powered Civil Economies

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<aside> <img src="/icons/list_lightgray.svg" alt="/icons/list_lightgray.svg" width="40px" /> Contents

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<aside> <img src="/icons/skip-forward_blue.svg" alt="/icons/skip-forward_blue.svg" width="40px" /> Backlog:

QTC Ontological Roadmap
Qubit-Tensors Archetype Development and Alignment with Resources
QT Algorithmic Architecture Prototype
Recalibration with Research on Chirality
Modular Design Refactoring, Streamlining Code and Refinement
Definitions about our main Classes, and Pseudocode.
Realigned the whole 1st Path to associate intensity with roll (past), phase with yaw (present), and frequency with pitch (future) for enhanced intelligence quantification, quantitation, and qualification..
[Last] Rewrote our Roadmap’s first and second path, which is actually an ontological seed - containing all the relative components of the whole Quantum-Tensor-Chain Protocol. So this page has been all rewritten from scratch. </aside>

Introduction

The QTC Journey aims to revolutionize the landscape of artificial intelligence by integrating quantum computing principles and embedding ethical decision-making at the core of AI systems. The first path in this journey, lays the foundational protocols for these innovations through a series of methodical steps.

In Path 1, we explore the theoretical and practical aspects of Qubit-Tensor-Chains (QTCs), a novel approach that redefines traditional tensor architecture. The objective is to create AI algorithms that are not only hardware agnostic but also inherently ethical and versatile. By leveraging the unique properties of quantum dots and their interactions with quantum mechanics, we aim to develop AI systems that are robust, scalable, and future-proof.

Key Objectives of Path 1:

Polarity Valence: Establishing a method to quantify intelligence by measuring the size and bandgap tunability of quantum dots. This step introduces the concept of Polarity Valence, which leverages quantum dot sizes to determine intelligence levels.
Polarity Balance: Developing a continuum for intelligence quantitation using the spectral emission properties of quantum dots. Polarity Balance focuses on the range of colors emitted, providing a nuanced measurement of intelligence.
Polarity Coherence: Qualifying different qualities or categories of intelligence through the brightness, or quantum yield, of quantum dots. Polarity Coherence uses the efficiency of light emission to distinguish various intelligence attributes.

Each step in this path integrates quantum principles with AI to enhance the capability and ethical grounding of the algorithms. By simulating quantum operations such as Qubit Roll, Yaw, and Pitch, we align classical physics with quantum mechanics, creating a seamless and powerful AI framework.