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
This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans

Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans This repository contains the implementation of the pap

Photogrammetry & Robotics Bonn 40 Dec 01, 2022
InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing

InterFaceGAN - Interpreting the Latent Space of GANs for Semantic Face Editing Figure: High-quality facial attributes editing results with InterFaceGA

GenForce: May Generative Force Be with You 1.3k Jan 09, 2023
An example project demonstrating how the Autonomous Learning Library can be used to build new reinforcement learning agents.

About This repository shows how Autonomous Learning Library can be used to build new reinforcement learning agents. In particular, it contains a model

Chris Nota 5 Aug 30, 2022
Parameterized Explainer for Graph Neural Network

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

Dongsheng Luo 89 Dec 12, 2022
Convex optimization for fun and profit.

CFMM Optimal Routing This repository contains the code needed to generate the figures used in the paper Optimal Routing for Constant Function Market M

Guillermo Angeris 183 Dec 29, 2022
ParmeSan: Sanitizer-guided Greybox Fuzzing

ParmeSan: Sanitizer-guided Greybox Fuzzing ParmeSan is a sanitizer-guided greybox fuzzer based on Angora. Published Work USENIX Security 2020: ParmeSa

VUSec 158 Dec 31, 2022
Supervised Classification from Text (P)

MSc-Thesis Module: Masters Research Thesis Language: Python Grade: 75 Title: An investigation of supervised classification of therapeutic process from

Matthew Laws 1 Nov 22, 2021
frida工具的缝合怪

fridaUiTools fridaUiTools是一个界面化整理脚本的工具。新人的练手作品。参考项目ZenTracer,觉得既然可以界面化,那么应该可以把功能做的更加完善一些。跨平台支持:win、mac、linux 功能缝合怪。把一些常用的frida的hook脚本简单统一输出方式后,整合进来。并且

diveking 997 Jan 09, 2023
TensorFlow implementation of "Variational Inference with Normalizing Flows"

[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co

YeongHyeon Park 7 Jun 08, 2022
Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

Ricochet Exchange 5 May 24, 2022
3D ResNet Video Classification accelerated by TensorRT

Activity Recognition TensorRT Perform video classification using 3D ResNets trained on Kinetics-400 dataset and accelerated with TensorRT P.S Click on

Akash James 39 Nov 21, 2022
RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds

RipsNet: a general architecture for fast and robust estimation of the persistent homology of point clouds This repository contains the code asscoiated

Felix Hensel 14 Dec 12, 2022
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model

Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for

Yash 2 Apr 07, 2022
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021 Update: 2021/03/11: update our new results. Now our T2T-ViT-14 w

YITUTech 1k Dec 31, 2022
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima

Wei Wei 59 Dec 26, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
Dual Attention Network for Scene Segmentation (CVPR2019)

Dual Attention Network for Scene Segmentation(CVPR2019) Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu Introduction W

Jun Fu 2.2k Dec 28, 2022
PyTorch implementation of MulMON

MulMON This repository contains a PyTorch implementation of the paper: Learning Object-Centric Representations of Multi-object Scenes from Multiple Vi

NanboLi 16 Nov 03, 2022
Atif Hassan 103 Dec 14, 2022