# QuaterMaster

### Overview

For the approach to the machine learning, we adopted a way to use physical theory like quantum annealing and neural network to enforce existing device and /or tool solutions such as conventional computing way, GPU and FPGA.

### Problems to solve

Machine Learning
Deep Learning
Crytography
Data Analysis
Finite Element Method

### Problems to research

Automatic layout and wiring system for integrated circuit
Route search method based on quantum annealing
Finding optimal formulation for drug and chemical substance development

## About Quantum Annealing Simulation

### Overview

We are simulating quantum annealing on a classical computor based on physical theory to discretization the Schrodinger equation.

### Path Integral

We discretize the Schrodinger equation using path integral to several Trotter Slice and calcurating the interaction between adjoining slices.

### Annealing

We control two way of annealing (Temperature and Quantum effect) by controlling two parameter of beta and gamma.

## About Machine Learning adopted to logical circuit

### Overview

In a basic physical theory, many phenomenon is written in Ising model . Basically these Ising Model has {-1,1} value of each spins.
To interpret these Ising model to Quantum annealing logical circuit, we are using QUBO to transform {-1,1} to {0.1}.

### Bit notation

To write a decimal number, we have to use some binary qubit to write these numbers.

### Logical Operations

For expample, to solve prime factorization ,we need to solove (N-pq)^2.Each of "p" and "q" is decimal number by binary qubit.
N^2 + 2pqN + (pq)^2 has calcuration of interaction over 2 factors. To solve 3 or 4 interaction of qubits, we need to decomposite these to 2 interaction.

## QuaterMaster{logical simulator based on Quantum Annealing}

Machine Spec
Tuning Mac Pro for Quantum annealing
-3.7 GHz quad Core Intel Xeon E5
-12Gb 1,866Mz DDR3 ECC memory
-Dual AMD FirePro D300 (2GB GDDR5 VRAM)
-256GB flash storage (PCIe)