Foreword

Recently, our team submitted a research paper called “Accelerating Simulated Quantum Annealing with GPU and Tensor Cores,” which was accepted by research paper of ISC high performance, 2022.

In this paper, we modify the SQA algorithm to increase the parallelism of the SQA algorithm and still guarantee the performance of the accuracy.

In general, SQA algorithms are often used to solve combinatorial optimization problems. We will first map the combinatorial optimization problem (e.g. traveling salesman problem (TSP)) that needs to solve to the appropriate Ising model. Different problems need to be mapped to different Ising models. Some Ising models like the fully-connected model, King graph model, EA model, etc., are referred to by the teams solving quantum annealing-related works (e.g. Tohoku’s SQA accelerated by FPGA and GPU, Toshiba Simulated Bifurcation Machine SBM).

Brefily Introduction

Original problem

This is the solution of qosf - cohort 5 - Task 3 - Find best results, including the original part and the bonus part of the full problem.

Briefly introducing this problem, we are playing tic-tac-toe. You are represented as ‘X’ and ‘O’ as your opponent.

And you find the situation in the figure below, next is your turn.
Try to develop a quantum algorithm to be able to find the best decisions with a higher probability within two rounds.

Introduction

• Coherent Ising Machine (CIM)，是一種用於解決Ising problem的量子機器，類似於退火(Annealing)。但卻是依據光學物理所建造的機器機器。隨著摩爾定律的衰落與量子電腦 量子信息的崛起，故survey了這方面的相關主題，CIM．內容略分為兩部分：

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