Welcome to The Eigensolver Quantum School, a quantum computing crash course designed by students for students.

Related tags

Deep LearningTEQS
Overview

TEQS

Welcome to The Eigensolver Quantum School, a crash course designed by students for students. The aim of this program is to take someone who has no QC knowledge and put through a five day crash course that puts them in the frame of mind necessary to learn via formal texts such as Nielsen and Chuang (which is the prize of our two day hackathon!)

TEQS Prerequisites

One of the beauties behind learning quantum computing is that on an elementary level, very few pre-requisites are required. At TEQS, the course is designed in a way where the only pre-requisites required are basic linear algebra and classical information processing. To ensure that everyone has those under their belts before attending the crash course, we made those three notebooks which we encourage everyone to read and solve the exercises.

  • Chapter 1 is on vectors and how they are used to represent the state of a qubit
  • Chapter 2 is on operators and how they are used to manipulate the state of a qubit
  • Chapter 3 is on Classical Information and Boolean Logic

Module Requirements

Lectures

Day 1:

Overview of mathematical prerequisites, brief introduction to quantum states and operators, and classical computing. Content available here.

Day 2:

Reduced quantum postulates from a quantum computing perspective and introduction to basic quantum circuits and simulators using Qiskit. Content available here.

Day 3:

The no-cloning theorem, quantum teleportation protocol, superdense coding, and BB84 cryptographic protocol. Content available here.

Day 4:

Quantum oracles, Deutsch's algorithm and how to construct a quantum circuit that implements them. Content available here.

Day 5:

IBM Quantum Fun Day! Introduction to RasQberry and Question and Answer Panel. Content available here.

Hackathon!

Welcome to the Eigensolvers Quantum School Hackathon! In the notebook found in this folder there are 4 problems covering all the material covered in the lectures. These problems have been designed for people coming from all different levels of experience in quantum computing. You will get a different certificate level based on the problems you completed:

  • First two: Beginner
  • First three: Intermediate
  • All four: Advanced

There are also prizes for the winners of the hackathon:

  • First Place: RasQberry - Premium
  • Second Place: RasQberry - All Inclusive
  • Third Place: RasQberry - Customizable DIY Kit
  • Fourth Place: Nielsen and Chuang

The ranking will be based on the weighted cost of the solutions for problem 3 and problem 4; as defined in the notebook.

To submit your solutions, fill out the form below, with the code that you write for each problem. https://forms.gle/KkA6gBbhrCZpWgnX8

The deadline for submission is Sunday (July 11th) 7pm Indian Standard Time. Remember, the ultimate goal is to have fun and learn some quantum computing while you're at it. All the best!

Owner
The Eigensolvers
The Eigensolvers
Json2Xml tool will help you convert from json COCO format to VOC xml format in Object Detection Problem.

JSON 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Json2Xml t

Nguyễn Trường Lâu 6 Aug 22, 2022
Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation".

I2V-GAN This repository is the official Pytorch implementation for ACMMM2021 paper "I2V-GAN: Unpaired Infrared-to-Visible Video Translation". Traffic

69 Dec 31, 2022
Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

CSRL Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning Python: 3

4 Apr 14, 2022
implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

MarginGAN This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning". 1."preliminary" is the imp

Van 7 Dec 23, 2022
Reproduction process of AlexNet

PaddlePaddle论文复现杂谈 背景 注:该repo基于PaddlePaddle,对AlexNet进行复现。时间仓促,难免有所疏漏,如果问题或者想法,欢迎随时提issue一块交流。 飞桨论文复现赛地址:https://aistudio.baidu.com/aistudio/competitio

19 Nov 29, 2022
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou

Xumin Yu 317 Dec 26, 2022
Attention mechanism with MNIST dataset

[TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention ma

YeongHyeon Park 12 Jun 10, 2022
Deep Learning Training Scripts With Python

Deep Learning Training Scripts DNN Frameworks Caffe PyTorch Tensorflow CNN Models VGG ResNet DenseNet Inception Language Modeling GatedCNN-LM Attentio

Multicore Computing Research Lab 16 Dec 15, 2022
[AAAI 2021] MVFNet: Multi-View Fusion Network for Efficient Video Recognition

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021) Overview We release the code of the MVFNet (Multi-View Fusion Network).

Wenhao Wu 114 Nov 27, 2022
pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

ABC:Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning, NeurIPS 2021 pytorch implementation of ABC : Auxiliary Balanced Class

Hyuck Lee 25 Dec 22, 2022
Scaling Vision with Sparse Mixture of Experts

Scaling Vision with Sparse Mixture of Experts This repository contains the code for training and fine-tuning Sparse MoE models for vision (V-MoE) on I

Google Research 290 Dec 25, 2022
Algorithmic trading using machine learning.

Algorithmic Trading This machine learning algorithm was built using Python 3 and scikit-learn with a Decision Tree Classifier. The program gathers sto

Sourav Biswas 101 Nov 10, 2022
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)

GSCNN This is the official code for: Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler

859 Dec 26, 2022
[ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated [talk] [poster] [appendix] Yuning You, Tianlong C

Shen Lab at Texas A&M University 80 Nov 23, 2022
Bootstrapped Representation Learning on Graphs

Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri

NerDS Lab :: Neural Data Science Lab 55 Jan 07, 2023
Official Implementation of DE-DETR and DELA-DETR in "Towards Data-Efficient Detection Transformers"

DE-DETRs By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-DETR and DELA-DETR in

Wen Wang 61 Dec 12, 2022
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
Multi-tool reverse engineering collaboration solution.

CollaRE v0.3 Intorduction CollareRE is a tool for collaborative reverse engineering that aims to allow teams that do need to use more then one tool du

105 Nov 27, 2022
Keras-1D-NN-Classifier

Keras-1D-NN-Classifier This code is based on the reference codes linked below. reference 1, reference 2 This code is for 1-D array data classification

Jae-Hoon Shim 6 May 18, 2021