Qubic Reference Miner

This Repo contains the reference implementation of the algoritm used in Qubic.

Requirement

• CPU: support at least AVX2 instruction set
• OS: Windows, Linux

Build

Windows

Visual Studio 2022

• Open Qiner.sln
• Build

Other Visual Studio versions

• Support generation using CMake with below command

# Assume in Qiner folder
mkdir build
cd build
"C:\Program Files\CMake\bin\cmake.exe" -G <Visual Studio Generator>
# Example: C:\Program Files\CMake\bin\cmake.exe" -G "Visual Studio 17 2022"

• Open Qiner.sln in build folder and build

Enable AVX512

• Open Qiner.sln
• Right click Qiner->[C/C++]->[Code Generation]->[Enable Enhanced Instruction Set] -> [...AVX512] -> OK

Linux

Currently support GCC and Clang

• Installed required libraries

For example,

• Ubuntu with GCC

sudo apt install build-essential

• Ubuntu with Clang

sudo apt install build-essential
sudo apt install clang

GCC

Run below command

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j8

Clang

Run below command

mkdir build
cd build
CC=clang CXX=clang++ cmake .. -DCMAKE_BUILD_TYPE=Release
make -j8

Enable AVX512

To enable AVX512, -DENABLE_AVX512=1 need to be parse in the cmake command.

Example,

# GCC
cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_AVX512=1

# Clang
CC=clang CXX=clang++ cmake .. -DCMAKE_BUILD_TYPE=Release -DENABLE_AVX512=1

Run

Qiner <IP> <Identity> [<number of threads>]

• number of threads: Optional, if not parse default number of cores will be used

Algorithm 2025-05-15

Definitions and precondition

• The random2 generator will be used consistently across the entire pipeline.
• Each neuron can hold a value of -1, 0, or 1.
• Synapse weights range within the continuous interval ([-1, 1]).
• Every neuron has exactly 2M outgoing synapses. Synapses with zero weight represent no connection.
• A synapse is considered to be owned by the neuron from which it originates.
• A mining seed is used to initialize the random values of both input and output neurons.
• The nonce and public key determine:
  • The random placement of input and output neurons on a ring,
  • The weights of synapses,
  • The method for selecting synapses during the evolution step.
• Symbols,
  • S: evolution step
  • P: max neurons population
  • R: the number of mismatch between expected output and computed ouput

I. ANN Structure Initialization

Given nonce and pubkey as seeds, and constants K, L, N, 2M:

1. Initialize K + L neurons arranged in a ring structure.
   • K input neurons and L output neurons are placed at random positions on the ring.
2. Initialize input and output neuron values randomly.
3. Initialize weights of 2M synapses with random values in the range [-1, 1] (i.e., -1, 0, or 1).
4. Convert neuron values to trits:
   • Keep 1 as is.
   • Change 0 to -1.
   • This step occurs only once.
5. Run initial tick simulation to initialize the R value.

---

II. Tick Simulation

1. For each neuron, compute the new value as: new_value = sum(weight × connected_neuron_value)
2. Clamp each neuron's value to the range [-1, 1].
3. Stop the tick simulation if any of the following conditions are met:

• All output neurons have non-zero values.
• N ticks have passed.
• No neuron values change.

---

III. Evolution and Simulation

1. Compute the initial R_best — the number of non-matching output bits.
2. Repeat the following mutation steps up to S times:
   • Randomly pick a synapse and change its weight:
     • Increase or decrease it by 1 (i.e., ±1).
     • If the new weight is within [-1, 1], proceed.
     • If the new weight becomes -2 or 2:
       • Revert the weight to its original value.
       • Insert a new neuron immediately after the connected neuron.
       • The new neuron:
         • Copies all incoming synapses from the original neuron.
         • Copies only the mutated outgoing synapse; all others are set to 0.
       • Remove any synapses exceeding the 2M limit per neuron.
3. Remove any neurons (except input/output) that:
   • Have all zero incoming synapses, or
   • Have all zero outgoing synapses.
4. Stop the evolution if the number of neurons reaches the population limit P.
5. Run Tick Simulation again.
6. Compute the new R value:
   • If R > R_best, discard the mutation.
   • If R ≤ R_best, accept the mutation and update R_best = R.