This guide will help you get started with the q_mini_wasm_v2 framework in minutes.

Before you begin, ensure you have:

• C++17 Compiler: GCC 7+, Clang 5+, or MSVC 2017+
• CMake: Version 3.14 or higher
• Git: For cloning the repository

git clone https://github.com/kennetholsenatm-gif/q_mini_wasm_v2.git
cd q_mini_wasm_v2/q_mini_wasm_v2

mkdir build
cd build
cmake ..
cmake --build .

mkdir build
cd build
cmake -DBUILD_TESTS=ON ..
cmake --build .

mkdir build
cd build
cmake -DUSE_SYCL=ON ..
cmake --build .

Run the test suite to ensure everything is working:

cd build
ctest --output-on-failure

You should see output indicating all tests passed.

Let’s create a simple program that demonstrates the core concepts of q_mini_wasm_v2.

Create a file named hello_ternary.cpp:

#include <iostream>
#include <vector>

#include "q_mini_wasm_v2/core/ternary/trit.hpp"
#include "q_mini_wasm_v2/core/stabilizer/tableau.hpp"
#include "q_mini_wasm_v2/core/moe/router.hpp"

using namespace q_mini_wasm_v2;

int main() {
    std::cout << "Hello, Ternary World!" << std::endl;
  // ... (truncated)
  // See source for complete code

# Navigate to build directory if not already there
cd build

# Compile (adjust include paths as needed)
g++ -std=c++17 -I../q_mini_wasm_v2 -o hello_ternary ../hello_ternary.cpp

./hello_ternary

Expected output:

Hello, Ternary World!
a = 1
b = -1
a + b = 0
a * b = -1

Created 2-qutrit tableau with entanglement
Measurement result: 0

• a = 1 (POSITIVE)
• b = -1 (NEGATIVE)
• a + b = 0 (1 + (-1) = 0 in GF(3))
• a * b = -1 (1 * (-1) = -1 in GF(3))

• Created a 2-qutrit tableau
• Applied Hadamard gate to put qutrit 0 in superposition
• Applied Controlled-SUM to entangle qutrits 0 and 1
• Measured qutrit 0, collapsing the entangled state

Now that you have a basic understanding, explore these topics:

• Ternary State Space: Understand GF(3) arithmetic and trit encoding
• Stabilizer Tableau: Learn about Clifford gates and quantum-inspired operations
• MoE Routing: Explore tropical geometry and expert selection

// Example: MoE Routing
core::moe::ExpertConfig config{8, 2, 4};  // 8 experts, Top-2, 4 routing qutrits
auto router = core::moe::create_moe_router(config);

std::vector<core::ternary::Trit> input = {
    core::ternary::Trit::POSITIVE,
    core::ternary::Trit::ZERO,
    core::ternary::Trit::NEGATIVE,
    core::ternary::Trit::POSITIVE
};

auto selected_experts = router->route_topk(input);
std::cout << "Selected " << selected_experts.size() << " experts" << std::endl;

• Forward-Forward Learning: Train networks without backpropagation
• SYCL Acceleration: Enable GPU acceleration for parallel operations
• Runtime Orchestration: Use async task execution

Solution: Ensure you have a C++17 compiler installed:

• Windows: Install Visual Studio 2017+ or MinGW-w64
• Linux: Install GCC 7+ (sudo apt install g++-7)
• macOS: Install Xcode Command Line Tools

Solution: Update CMake to version 3.14+:

# Ubuntu/Debian
sudo apt remove cmake
sudo snap install cmake --classic

# macOS
brew upgrade cmake

Solution: Ensure you built with tests enabled:

cmake -DBUILD_TESTS=ON ..
cmake --build .
ctest --output-on-failure

If you encounter issues:

1. Check the Architecture Overview
2. Review the [API Reference](API-Core Reference.md)
3. Consult the Build Guide
4. Open an issue on GitHub

Continue your learning journey:

• Architecture Overview - Understand the system design
• [API Reference](API-Core Reference.md) - Complete API documentation
• Build Guide - Detailed build instructions
• Contributing Guide - How to contribute