Dylan  Iqbal

Dylan Iqbal


Learn Quantum Mechanics from the Beginning to the End

Quantum Physics Full Course | Quantum Mechanics Course

Quantum physics also known as Quantum mechanics is a fundamental theory in physics that provides a description of the physical properties of nature at the scale of atoms and subatomic particles. It is the foundation of all Quantum Physics including quantum chemistry, quantum field theory, quantum technology, and quantum information science.

In this course you will learn about Quantum Mechanics from the beginning to the end. The following topics of Quantum mechanics have been discussed in this course:

⭐️  Table of Contents ⭐️  
⌨️  (0:00:00) Introduction to quantum mechanics
⌨️  (0:16:23) The domain of quantum mechanics
⌨️  (0:24:18) Key concepts of quantum mechanics
⌨️  (0:34:04) A review of complex numbers for QM
⌨️  (0:48:12) Examples of complex numbers
⌨️  (1:01:47) Probability in quantum mechanics
⌨️  (1:12:17) Variance of probability distribution
⌨️  (1:26:16) Normalization of wave function
⌨️  (1:51:47) Position, velocity and momentum from the wave function
⌨️  (2:10:59) Introduction to the uncertainty principle
⌨️  (2:24:32) Key concepts of QM - revisited
⌨️  (2:37:45) Separation of variables and Schrodinger equation
⌨️  (3:09:55) Stationary solutions to the Schrodinger equation
⌨️  (3:15:47) Superposition of stationary states
⌨️  (3:25:37) Potential function in the Schrodinger equation
⌨️  (3:48:10) Infinite square well (particle in a box)
⌨️  (4:00:58) Infinite square well states, orthogonality - Fourier series
⌨️  (4:08:07) Infinite square well example - computation and simulation
⌨️  (4:39:27) Quantum harmonic oscillators via ladder operators
⌨️  (5:16:48) Quantum harmonic oscillators via power series
⌨️  (5:28:32) Free particles and Schrodinger equation
⌨️  (5:34:37) Free particles wave packets and stationary states
⌨️  (6:10:33) Free particle wave packet example
⌨️  (6:13:43) The Dirac delta function
⌨️  (6:20:49) Boundary conditions in the time independent Schrodinger equation
⌨️  (6:24:39) The bound state solution to the delta function potential TISE
⌨️  (6:43:29) Scattering delta function potential
⌨️  (6:55:49) Finite square well scattering states
⌨️  (7:07:39) Linear algebra introduction for quantum mechanics
⌨️  (7:10:34) Linear transformation
⌨️  (7:13:04) Mathematical formalism is Quantum mechanics
⌨️  (7:37:52) Hermitian operator  eigen-stuff
⌨️  (8:01:23) Statistics in formalized quantum mechanics
⌨️  (8:24:26) Generalized uncertainty principle
⌨️  (8:54:36) Energy time uncertainty
⌨️  (9:16:33) Schrodinger equation in 3d
⌨️  (9:19:56) Hydrogen spectrum
⌨️  (9:31:14) Angular momentum operator algebra
⌨️  (9:57:17) Angular momentum eigen function
⌨️  (10:18:08) Spin in quantum mechanics
⌨️  (10:22:23) Two particles system
⌨️  (10:58:03) Free electrons in conductors
⌨️  (11:09:23) Band structure of energy levels in solids

#quantum #developer 

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Learn Quantum Mechanics from the Beginning to the End
Jon  Gislason

Jon Gislason


Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Helpful Code Generator for The README and Other Markdown Files


Helpful code generator for the README and other markdown files.


1. Install or update readme_helper:

flutter pub global activate readme_helper

2. Start using these "magical" comment commands:

Insert code example from external file:
<!-- #code path/to/file.dart -->

Create a table of contents based on used headlines:
<!-- #toc -->

This will include another markdown file:
<!-- #include path/to/other_markdown.md -->

3. Run readme_helper for current project:

flutter pub global run readme_helper

Table of Contents






A good documentation is the key in connecting package creators and developers.

Embedding code examples is essential, but they might deprecate over the time. This tool enables you to use external code files, the correctness is ensured by your IDE.

Additional tooling like markdown inclusion or table of contents generation, will help you save time.


The readme_helper is a Dart application, that can be installed or updated with:

flutter pub global activate readme_helper

You can run the readme_helper with:

flutter pub global run readme_helper

It will take care of all markdown files within the current directory and it's sub-directories.

Alternately you can process only a single file with:

flutter pub global run readme_helper path/to/file.md


You can specify commands by using HTML comments in your markdown files. Each readme_helper command starts with a #:

<!-- #command argument -->

Code embedding

You can embed external files by defining the relative path to it.

<!-- #code path/to/code.dart -->

This will add a code block with the content of that file.

Scope comments

You can use comments to control the part of the external file shown.

import 'dart:math';

// #begin
class MyClass {
  // #skip
  int someMethod() {
    return Random().nextInt(1);
  // #resume

  String interestingMethod() {
    return 'Foo';
// #end

This will add the following code block:

class MyClass {

  String interestingMethod() {
    return 'Foo';


By indenting the // #begin scope comments, you can hint to remove leading whitespace.

class AnotherClass {
  // #begin
  int importantMethod() {
    return 42;
  // #end

This will add the following code block:

int importantMethod() {
  return 42;

Table of Contents generation

The readme_helper will scan all markdown headlines (## and ###) and generate a table of contents.

# project_name

<!-- #toc -->

## chapter a
### section 1
### section 2

## chapter b
### section 3
### section 4

This will create something like this:

Include markdown files

You can include parts from other files into the current markdown file, by using an include:

<!-- #include path/to/part.md -->

Generate line breaks

By default you can't have more then one new line. For esthetics you might want to extend this limit.

<!-- #space 2 -->

This will generate line breaks with &nbsp; characters.

Use this package as a library

Depend on it

Run this command:

With Dart:

 $ dart pub add readme_helper

With Flutter:

 $ flutter pub add readme_helper

This will add a line like this to your package's pubspec.yaml (and run an implicit dart pub get):

  readme_helper: ^0.1.1

Alternatively, your editor might support dart pub get or flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:readme_helper/code.dart';
import 'package:readme_helper/code_utils.dart';
import 'package:readme_helper/include.dart';
import 'package:readme_helper/lines.dart';
import 'package:readme_helper/process_file.dart';
import 'package:readme_helper/space.dart';
import 'package:readme_helper/toc.dart'; 

Download Details:

Author: felixblaschke

Source Code: https://github.com/felixblaschke/readme_helper

#flutter #markdown 

Jerad  Bailey

Jerad Bailey


Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Abigail  Cassin

Abigail Cassin


Quantum Machine Learning: learning on neural networks

My last articles tackled Bayes nets on quantum computers (read it here!), and k-means clustering, our first steps into the weird and wonderful world of quantum machine learning.

This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.

In case you aren’t up to speed on neural nets, don’t worry — we’re starting with neural nets 101.

What even is a (classical) neural network?

Almost everyone has heard of neural networks — they’re used to run some of the coolest tech we have today — self driving cars, voice assistants, and even the software that generates super realistic pictures of famous people doing questionable things.

What makes them different from regular algorithms is that instead of having to write down a set of rules, we need to provide networks with examples of the problem we want it to solve.

#machine-learning #quantum-computing #artificial-intelligence #quantum-machine-learning #math