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
Repository for the paper "Exploring the Sensory Spaces of English Perceptual Verbs in Natural Language Data"

Sensory Spaces of English Perceptual Verbs This repository contains the code and collocational data described in the paper "Exploring the Sensory Spac

David Peng 0 Sep 07, 2021
Official code implementation for "Personalized Federated Learning using Hypernetworks"

Personalized Federated Learning using Hypernetworks This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [

Aviv Shamsian 121 Dec 25, 2022
[NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Anima Anandkumar, an

VITA 112 Nov 07, 2022
This project is the PyTorch implementation of our CVPR 2022 paper:

Requirements and Dependency Install PyTorch with CUDA (for GPU). (Experiments are validated on python 3.8.11 and pytorch 1.7.0) (For visualization if

Lei Huang 23 Nov 29, 2022
H&M Fashion Image similarity search with Weaviate and DocArray

H&M Fashion Image similarity search with Weaviate and DocArray This example shows how to do image similarity search using DocArray and Weaviate as Doc

Laura Ham 18 Aug 11, 2022
Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

clip-text-decoder Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script. Example Predi

Frank Odom 36 Dec 21, 2022
Implementation of FSGNN

FSGNN Implementation of FSGNN. For more details, please refer to our paper Experiments were conducted with following setup: Pytorch: 1.6.0 Python: 3.8

19 Dec 05, 2022
Code for our ALiBi method for transformer language models.

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation This repository contains the code and models for our paper Tra

Ofir Press 211 Dec 31, 2022
Model-based 3D Hand Reconstruction via Self-Supervised Learning, CVPR2021

S2HAND: Model-based 3D Hand Reconstruction via Self-Supervised Learning S2HAND presents a self-supervised 3D hand reconstruction network that can join

Yujin Chen 72 Dec 12, 2022
Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification

Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-Identification Suncheng Xiang Shanghai Jiao Tong University Over

SunchengXiang 68 Dec 13, 2022
Non-Official Pytorch implementation of "Face Identity Disentanglement via Latent Space Mapping" https://arxiv.org/abs/2005.07728 Using StyleGAN2 instead of StyleGAN

Face Identity Disentanglement via Latent Space Mapping - Implement in pytorch with StyleGAN 2 Description Pytorch implementation of the paper Face Ide

Daniel Roich 58 Dec 24, 2022
Implement slightly different caffe-segnet in tensorflow

Tensorflow-SegNet Implement slightly different (see below for detail) SegNet in tensorflow, successfully trained segnet-basic in CamVid dataset. Due t

Tseng Kuan Lun 364 Oct 27, 2022
Laplacian Score-regularized Concrete Autoencoders

Laplacian Score-regularized Concrete Autoencoders Requirements: torch = 1.9 scikit-learn = 0.24 omegaconf = 2.0.6 scipy = 1.6.0 matplotlib How to

JS 6 Dec 07, 2022
Manim is an engine for precise programmatic animations, designed for creating explanatory math videos

Manim is an engine for precise programmatic animations, designed for creating explanatory math videos. Note, there are two versions of manim. This rep

Grant Sanderson 49k Jan 09, 2023
Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations"

Source code for our paper "Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations" this repository is maintained by bo

Yuhan Liu 24 Nov 29, 2022
Face recognition. Redefined.

FaceFinder Use a powerful CNN to identify faces in images! TABLE OF CONTENTS About The Project Built With Getting Started Prerequisites Installation U

BleepLogger 20 Jun 16, 2021
Notebooks em Python para Métodos Eletromagnéticos

GeoSci Labs This is a repository of code used to power the notebooks and interactive examples for https://em.geosci.xyz and https://gpg.geosci.xyz. Th

Victor Cezar Tocantins 1 Nov 16, 2021
This is the repo of the manuscript "Dual-branch Attention-In-Attention Transformer for speech enhancement"

DB-AIAT: A Dual-branch attention-in-attention transformer for single-channel SE

Guochen Yu 68 Dec 16, 2022
Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face Manipulation" published in CVPR 2020.

FFD Source Code Provided is code that demonstrates the training and evaluation of the work presented in the paper: "On the Detection of Digital Face M

88 Nov 22, 2022
A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

Benedek Rozemberczki 202 Dec 27, 2022