A quantum game modeling of pandemic (QHack 2022)

Overview

Abstract

In the regime of a global pandemic, leaders around the world need to consider various possibilities and take conscious actions to protect their citizens from the infectious virus. In the quantum world that we model in this game, every possible situation exists as a superposed state. Nothing is decisive at all. You, as the leader of this quantum city, need to suppress the possibility, or amplitude of states representing bad situations. Lastly, the mandatory PCR test for every citizen is waiting you---it 'measures' the city and will show whether your policies rescued the city or not. Predict, act, and measure!

The Game

Objectives

  • Obtain negative result for everyone at the last PCR test.

Contents

  • Mode
    In this game, there are two modes: Pure Quandemic and Mixed Quandemic. From the former one, the state of the citizens is always pure state. All the actions are unitary. On the other hand, when using the latter one, the state of the citizens can be mixed state. Considering a density matrix will be a good strategy. Most of actions are unitary, however, swapping two citiznes lead to non-unitary evolution. More details are described at 'Regular Action: Move Citizens (Swap)'. Input : write 1(0) if you want to play 'Mixed Quandemic'('Pure Quandemic'). ex) 1

  • Level
    The level indicates the initial number of infected people. However, indices of infected people are selected randomly. Input : write the number of level. ex) 3

  • Citizens
    A quantum circuit with N by M qubits represents a city that N*M citizens live with a deadly virus. 0's and 1's appearing on the computational basis of this system corresponds to healthy and infected states, respectively. Since the people live in a quantum world, the city stays in a superposition of possible infection states!

  • Regular Action: PCR Testing (Single Person)
    A PCR test corresponds to measurement on a specific qubit, or a citizen of this city. Not only obtains a decisive result about the citizen's infection status, the test destroys possibility of the city to be in states which counter the test result. In quantum-like words, the measurement projects previous state into a subspace contains the measured result. Input : write the index of person you want to inspect. ex) 4

  • Special Action: PCR Testing (Total Inspection)
    For sake of the player, one can measure states of all qubits at once for only one time during the game. It will remove superposition of the city's state, but the state will quickly branch and involve possibilities as time goes on. Input : write 1(0) if you want(do not want) to do the action. ex) 1

  • Regular Action: Move Citizens (Swap)
    In each turn, player should choose pairs of citizens to swap position. However, when a player use 'Mixed Quandemic' mode, they might additionally catch the virus since the swapped citizens can be exposed to the contaminated environment while swapping each other. The newly possible infected state is involved to the game as superposition. Simply, a quantum SWAP gate and a Kraus operator(only for 'Mixed Quandemic' mode) which puts 0 to 1 at a fixed possibility successively applied for each pair of citizens that the player selected. Players are allowed to swap 'neighboring' citizens only. Input : write the pairs of people's indices for inspection. If you want to inspect (0,1) and (3,4) --> ex) 0,1 3,4

  • Regular Action: Send Hospital
    There are two hospitals in this city placed at the certain area.

    • The 'H' hospital
      The 'H' hospital is placed on boundaries of the city. For example, in 3x3 city, 'H' hospital is placed at position 0, 1, 2, 3, 5, 6, 7, 8. The 'H' hospital works by applying Hadamard gate if player selects its position. Be careful that it might increase probability of infection if it is used in a wrong way!

    • The Pauli's X hospital
      The Pauli's X hospital is placed at the center of the city. It acts to the citizen at the center by applying X gate. So the hospital will cure a citizen if one is infected, but it will infect a healthy one at the same time! This hospital has the perfect medicine, but it is located at the center of the city.. It is really easy to get infected via passing through the central city.

Input : write the indices of people who wants to go to the hospital. ex) 0 1 3

In each turn, the player should select which citizens to send hospital. It is only possible to send citizens that are placed on the hostpial area.

  • The last, mandatory PCR test
    This test decides whether your critical choices during the pandemic were successful or not. This very final operation measures all qubits of the system as the total survey. Even if a single 1 exists in your final state, it will move, copy itself and spread throughout your city again. No way! The game's objective is to obtain the result |00...00> and to free your city from the pandemic forever! Input : write 1(0) if you want(do not want) to do the action. ex) 1

Demonstration

Title_Image

We first select pairs of citizen to swap position, indicated as blue edges. Then, select which citizens to send hospital, indicated as light-red boxes. Press 'Next' button to progress to next step. We can either check one person's PCR testing result, or use the total PCR inspection chance (limited to once per game). Execute GUI version of the game by python3 GUI_Quandemics.py.

Captured Scene

  • Example of the 'GUI' version

Title_Image

It is the interim state of the 'GUI' version game. #0 person visited the 'H' hospital. By the way, we had inspected the PCR test for the #2 person, and his/her result was positive.
Owner
Yoonjae Chung
KAIST EE & Physics Undergraduate
Yoonjae Chung
A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Improved Adversarial Systems for 3D Object Generation and Reconstruction: This is a repository for the paper "Improved Adversarial Systems for 3D Obje

Edward Smith 188 Dec 25, 2022
CausaLM: Causal Model Explanation Through Counterfactual Language Models

CausaLM: Causal Model Explanation Through Counterfactual Language Models Authors: Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart Abstract: Understan

Amir Feder 39 Jul 10, 2022
Neon: an add-on for Lightbulb making it easier to handle component interactions

Neon Neon is an add-on for Lightbulb making it easier to handle component interactions. Installation pip install git+https://github.com/neonjonn/light

Neon Jonn 9 Apr 29, 2022
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21)

EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation (CVPR'21) Citation If y

addisonwang 18 Nov 11, 2022
An open source Python package for plasma science that is under development

PlasmaPy PlasmaPy is an open source, community-developed Python 3.7+ package for plasma science. PlasmaPy intends to be for plasma science what Astrop

PlasmaPy 444 Jan 07, 2023
A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

Blitz - Bayesian Layers in Torch Zoo BLiTZ is a simple and extensible library to create Bayesian Neural Network Layers (based on whats proposed in Wei

Pi Esposito 722 Jan 08, 2023
A semantic segmentation toolbox based on PyTorch

Introduction vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation

407 Dec 15, 2022
A large-scale benchmark for co-optimizing the design and control of soft robots, as seen in NeurIPS 2021.

Evolution Gym A large-scale benchmark for co-optimizing the design and control of soft robots. As seen in Evolution Gym: A Large-Scale Benchmark for E

121 Dec 14, 2022
Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?" submitted to TOSEM

tosem2021-personality-rep-package Replication package for the manuscript "Using Personality Detection Tools for Software Engineering Research: How Far

Collaborative Development Group 1 Dec 13, 2021
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 07, 2022
DataCLUE: 国内首个以数据为中心的AI测评(含模型分析报告)

DataCLUE: A Benchmark Suite for Data-centric NLP You can get the english version of README. 以数据为中心的AI测评(DataCLUE) 内容导引 章节 描述 简介 介绍以数据为中心的AI测评(DataCLUE

CLUE benchmark 135 Dec 22, 2022
This repository introduces a short project about Transfer Learning for Classification of MRI Images.

Transfer Learning for MRI Images Classification This repository introduces a short project made during my stay at Neuromatch Summer School 2021. This

Oscar Guarnizo 3 Nov 15, 2022
Learning with Noisy Labels via Sparse Regularization, ICCV2021

Learning with Noisy Labels via Sparse Regularization This repository is the official implementation of [Learning with Noisy Labels via Sparse Regulari

Xiong Zhou 38 Oct 20, 2022
Job Assignment System by Real-time Emotion Detection

Emotion-Detection Job Assignment System by Real-time Emotion Detection Emotion is the essential role of facial expression and it could provide a lot o

1 Feb 08, 2022
这是一个yolox-pytorch的源码,可以用于训练自己的模型。

YOLOX:You Only Look Once目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤

Bubbliiiing 613 Jan 05, 2023
Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid

SPN: Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid Code for Fully Context-Aware Image Inpainting with a Learned Semantic Pyrami

12 Jun 27, 2022
Official implementation of the paper Momentum Capsule Networks (MoCapsNet)

Momentum Capsule Network Official implementation of the paper Momentum Capsule Networks (MoCapsNet). Abstract Capsule networks are a class of neural n

8 Oct 20, 2022
Unofficial PyTorch implementation of the Adaptive Convolution architecture for image style transfer

AdaConv Unofficial PyTorch implementation of the Adaptive Convolution architecture for image style transfer from "Adaptive Convolutions for Structure-

65 Dec 22, 2022
Implements an infinite sum of poisson-weighted convolutions

An infinite sum of Poisson-weighted convolutions Kyle Cranmer, Aug 2018 If viewing on GitHub, this looks better with nbviewer: click here Consider a v

Kyle Cranmer 26 Dec 07, 2022
Explanatory Learning: Beyond Empiricism in Neural Networks

Explanatory Learning This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks". Datasets Download the datasets

GLADIA Research Group 10 Dec 06, 2022