[Note] Quantum Computation and Quantum Information - Final exam
期末考詳解,原文書:
Quantum Computation and Quantum Information, Michael A. Nielsen & Isaac L. Chuang
- 班平均:64
[Note] Quantum Computation and Quantum Information - Chapter 6: Quantum search algorithms
[Note] Quantum Computation and Quantum Information - Chapter 5: The quantum Fourier transform and its applications
[Note] Quantum Computation and Quantum Information - Midterm exam
期中考詳解,原文書:
Quantum Computation and Quantum Information, Michael A. Nielsen & Isaac L. Chuang
- 班平均:57.7
- 標準差:13.9
[Note] Quantum Computation and Quantum Information - Chapter 4: Quantum Circuits
[Note] Quantum Computation and Quantum Information - Chapter 3: Introduction to compuer science
[Note] Quantum Computation and Quantum Information - Chapter 2: Introduction to quantum mechanics
上課筆記,原文書:
Quantum Computation and Quantum Information, Michael A. Nielsen & Isaac L. Chuang
2.1 Linear algebra
Notation | Description |
---|---|
$z^*$ | Complex conjugate of the complex number $z$. $(1+i)^*=1-i$ |
$\vert\psi\rangle$ | Column vector. Also known as a ket |
$\langle\psi\vert$ | Row vector dual to $\vert\psi\rangle$. Also known as bra |
$\langle \varphi\vert\psi\rangle$ | Inner product between the vectors $\vert\varphi\rangle$ and $\vert\psi\rangle$ |
$\vert\varphi\rangle \otimes\vert\psi\rangle$ | Tensor product of $\vert\varphi\rangle$ and $\vert\psi\rangle$ |
$A^*$ | Complex conjugate of the $A$ matrix |
$A^T$ | Transpose of the $A$ matrix |
$A^\dagger$ | Hermitian conjugate or adjoint of the $A$ matrix, $A^\dagger=(A^T)^*$. $\left[\begin{matrix}a & b\ c & d\end{matrix}\right]^\dagger = \left[\begin{matrix} a^* & c^*\\b^* & d^*\end{matrix}\right]$ |
$\langle \varphi\vert A\vert\psi \rangle$ | Inner product between $\vert\varphi\rangle$ and $A\vert\psi\rangle$. Equivalently, inner product between $A^\dagger\vert\varphi\rangle$ and $\vert\psi\rangle$. |
[Tuto] 夢魘のCUDA: 使用 Preconditioned Conjugate Gradient 輕鬆解決大型稀疏線性方程組
圖片來源:http://fourier.eng.hmc.edu/e176/lectures/NM/node29.html
閱讀難度 ✦✦✧✧✧
特別感謝 冠大大、王大大。
《夢魘のCUDA》是 CUDA Programming 系列,此系列不會介紹任何 CUDA 的基礎知識,而會介紹一些 CUDA 相關的應用。本篇作為《夢魘のCUDA》系列的首篇文,將會介紹既實用又不實用的兩套 CUDA 內建 Library — cuBLAS / cuSPARSE;除此之外,本篇也會講解如何使用這兩套 Library 實作出經典應用 — Preconditioned Conjugate Gradient。之所以說是經典應用的原因是因為,cuSPARSE 幾乎是為了這個應用而誕生的。
[Note] AuTO: Scaling Deep Reinforcement Learnign for Datacenter-Scale Automatic Traffic Optimization
原論文:Li Chen, J. Lingys, Kai Chen and Feng Liu. AuTO: Scaling Deep Reinforcement Learnign for Datacenter-Scale Automatic Traffic Optimization. SIGCOMM 2018.