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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.