Learn about quantum computing and algorithm on quantum computing

Overview

quantum_computing


this repo contains everything i learn about quantum computing and algorithm on quantum computing

what is aquantum computing

quantum computing is an area of study focused on the development of computer based technologies centered around the principles of quantum theory. quantum computing uses a combination of bits to perform specific computational taks. all at much higher efficiency than their classical counterparts. development of quantum computers mark a leap forward in computing capability, with massive performance gains for specifig use cases. for example quantum computing excels at like simulations.

working with quantum

to code any quantum circuit, step

  1. build design a quantum circuit that represent the problem considering
  2. execute run a circuits on a backedn, either a system or a simulator
  3. analyze calculate summary statistic and visualize the result of circuit jobs

simple code to build circuit

circuit = QuantumCircuit(2,2)

# add a H gate on qubit 0
circuit.h(0)

# add a CX(CNOT) gate control qubit 0 and target qubit 1
circuit.cx(0, 1)

# map the quantum measurement to the classical bits
circuit.measure([0, 1], [0,1])

explanation

first, initialize two qubits in the zero state and two classical bits in the zero state in the quantum circuit called circuit:

circuit = QuantumCircuit(2, 2)

next, add gates that manipulate th qubits in circuit to frm bell state:

codingcogs

in this state, there is a 50% chance of finding both qubits to have the value 0 and a 50% chance of finding both qubits to have the value 1.

gates:

  • QuantumCircuit.h(0)

    A handmard gate (h) on qubit 0, which puts it into a superposition state

  • QuantumCircuit.cx(0, 1)

    A controlled-NOT operantion (CX) on control qubit 0 and target qubit 1, putting the qubits in an entangled state.

  • QuantumCircuit.measure([0, 1], [0, 1])

    The first argument indexes the qubits; the second argument indexes the classical bits, then the n qubit's measueremtn result ill be stroed in the n classical bit.

circuit.h(0)
circutir.cx(0, 1)
circuit.measure([0, 1], [0, 1])
Owner
arfy slowy
a simple person for focusing on fixing some code ( especially python ) and make sure the application works very well
arfy slowy
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 02, 2023
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
Official repository for the paper "Instance-Conditioned GAN"

Official repository for the paper "Instance-Conditioned GAN" by Arantxa Casanova, Marlene Careil, Jakob Verbeek, Michał Drożdżal, Adriana Romero-Soriano.

Facebook Research 510 Dec 30, 2022
Pull sensitive data from users on windows including discord tokens and chrome data.

⭐ For a 🍪 Pegasus Pull sensitive data from users on windows including discord tokens and chrome data. Features 🟩 Discord tokens 🟩 Geolocation data

Addi 44 Dec 31, 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.

[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.

VITA 112 Nov 07, 2022
Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction".

TGIN Tensorflow implementation of our method: "Triangle Graph Interest Network for Click-through Rate Prediction". Files in the folder dataset/ electr

Alibaba 21 Dec 21, 2022
Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images

Multimodal Co-Attention Transformer (MCAT) for Survival Prediction in Gigapixel Whole Slide Images [ICCV 2021] © Mahmood Lab - This code is made avail

Mahmood Lab @ Harvard/BWH 63 Dec 01, 2022
Pytorch code for "DPFM: Deep Partial Functional Maps" - 3DV 2021 (Oral)

DPFM Code for "DPFM: Deep Partial Functional Maps" - 3DV 2021 (Oral) Installation This implementation runs on python = 3.7, use pip to install depend

Souhaib Attaiki 29 Oct 03, 2022
The end-to-end platform for building voice products at scale

Picovoice Made in Vancouver, Canada by Picovoice Picovoice is the end-to-end platform for building voice products on your terms. Unlike Alexa and Goog

Picovoice 318 Jan 07, 2023
Lightweight Face Image Quality Assessment

LightQNet This is a demo code of training and testing [LightQNet] using Tensorflow. Uncertainty Losses: IDQ loss PCNet loss Uncertainty Networks: Mobi

Kaen 5 Nov 18, 2022
Compact Bidirectional Transformer for Image Captioning

Compact Bidirectional Transformer for Image Captioning Requirements Python 3.8 Pytorch 1.6 lmdb h5py tensorboardX Prepare Data Please use git clone --

YE Zhou 19 Dec 12, 2022
Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator

DRL-robot-navigation Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gra

87 Jan 07, 2023
Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Intro to Deep Learning Materials are part of North South University (NSU) Intro to Deep Learning with PyTorch workshop series. (Slides) Related materi

Hasib Zunair 9 Jun 08, 2022
Node-level Graph Regression with Deep Gaussian Process Models

Node-level Graph Regression with Deep Gaussian Process Models Prerequests our implementation is mainly based on tensorflow 1.x and gpflow 1.x: python

1 Jan 16, 2022
Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

ReFine: Multi-Grained Explainability for GNNs This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 20

Shirley (Ying-Xin) Wu 47 Dec 16, 2022
Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks by Ángel López García-Arias, Masanori Hashimoto, Masato Motomura, and J

Ángel López García-Arias 4 May 19, 2022
Automatic Image Background Subtraction

Automatic Image Background Subtraction This repo contains set of scripts for automatic one-shot image background subtraction task using the following

Oleg Sémery 6 Dec 05, 2022
Simple machine learning library / 簡單易用的機器學習套件

FukuML Simple machine learning library / 簡單易用的機器學習套件 Installation $ pip install FukuML Tutorial Lesson 1: Perceptron Binary Classification Learning Al

Fukuball Lin 279 Sep 15, 2022
PyTorch version implementation of DORN

DORN_PyTorch This is a PyTorch version implementation of DORN Reference H. Fu, M. Gong, C. Wang, K. Batmanghelich and D. Tao: Deep Ordinal Regression

Zilin.Zhang 3 Apr 27, 2022
CVPR 2021

Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-image Translation [Paper] | [Poster] | [Codes] Yahui Liu1,3, Enver Sangineto1,

Yahui Liu 37 Sep 12, 2022