Hello, World!

~ No Day But Today ~

Recommend Series

Quantum-Mechanics (11)

Notes about Quantum Mechanics. Include class notes of @Wang Fa and other book notes

Field-Theory (10)

Notes about Quantum Field Theory. Include class notes of @Zheng Hanqing and other book notes

Go-Learning (2)
From RNN to ChatGPT
  • Mar 5, 2023
  • 13 minutes read
This note roughly introduces the development in natural language processing during the past decades. The RNN, LSTM, attention, and Transformer would be covered. Then, the technologies behind the recent popular ChatGPT will be shared, including GPT-series models, Prompts, and how to train language model fitting human intent
对一般刚体的定点转动的统计力学分析
  • Jul 15, 2022
  • 9 minutes read
在统计力学中, 我们通常会关注正则动量与坐标. 因为由它们确定的相空间体积元在正则变换下不变. 但在我们实际进行计算时, 却并非总是正则动量与坐标的表达最为方便. 一个例子就是刚体力学中, 我们会偏爱使用转动惯量和惯量主轴的方法来讨论问题. 然而作为角动量, 它们彼此之间并不对易, 因而在处理统计力学积分时往往带来困惑: 我们能使用它们作为被积变量吗? 进一步的, 用刚体转动模型理解多原子分子热容时同样会遇到这个问题. 本文旨在对这个问题给出解答: 在讨论不依赖转动构型的力学量时, 使用主轴角动量作为动力学变量是完全可行的. 变量替换的 Jacobian 只是转动构型的函数.
Introduction to Measurement Based Quantum Computing
  • May 19, 2022
  • 15 minutes read
In this note, we will generally discuss the basic conceptions of measurement-based quantum computing(MBQC). We focus on the workflow of MBQC and how to transpile a circuit-based model to MBQC. The parallization for quantum computing and other optimizations are not fully covered but the references are offered. Finally, we discuss the development of MBQC in theoretical perspective.
Prove the Irrationality of Square Root of 2
  • Feb 19, 2022
  • 2 minutes read
Various methods of proving the irrationality of the square root of integer 2. Including the method by contradiction and the direct way.
Machine Learning in Quantum Mechanics, Up to 2021
  • Dec 12, 2021
  • 21 minutes read
In the recent(at 2021) decades, machine learning algorithms have been popular in almost all disciplines of science and even art. People have found out that a huge share of research and production problems can be viewed as instances of basic tasks that could get well solved with the modern powerful hardware and algorithms. In physics, there has been already plenty of applications covering data manipulation and model/theory discovering. However, just like those famous algorithms in the past, machine learning has its own caveats. Researchers should get well understanding about the boundary of algorithms' ability. Being a well-trained user instead of package importer would be the new requirements of people in related fields. In this review, we will discuss the success applications of machine learning in quantum physics in recent years and the potential limitation. Then the reader could get a clearer picture on the development of machine learning and quantum physics frontier.