chrissimpkins

import commonmark

with open('test.md', 'r') as myfile:
  text = myfile.read()

parser = commonmark.Parser()
ast = parser.parse(text)

# Returns the text from markdown, stripped of the markdown syntax itself
def ast2text(astNode):

primaryobjects

Quantum Computing @ Grace Hopper Conference 2019 GHC19

I had recently attended the Grace Hopper 2019 conference in Orlando, and had the opportunity to speak with researchers from IBMQ and Qiskit about the current state of quantum computing. Although, I did not get a chance to obtain a ticket to IBMQ's Universal Studios event at The Wizarding World of Harry Potter (the line was extremely long!), I still wanted to share some of the topics surrounding the emerging tech of quantum computing.

I spoke with Cihan Kurter, Research Scientist at IBM Watson, after her presentation titled, "Near-Term Applications of Quantum Computers", and asked, how exactly does the mysterious IBMQ qubit work?

How does a qubit work?

cerebrate

WARNING

THIS GIST IS EXTREMELY OBSOLETE. DO NOT FOLLOW THESE INSTRUCTIONS. SERIOUSLY.

IF YOU IGNORE THE ABOVE WARNING, YOU AGREE IN ADVANCE THAT YOU DIDN'T GET THESE INSTRUCTIONS FROM ME, THAT I WARNED YOU, AND THAT I RESERVE THE RIGHT TO POINT AND LAUGH MOCKINGLY IF AND WHEN SOMETHING BREAKS HORRIBLY.

I'll do a write-up of current custom-kernel procedures over on Random Bytes ( https://randombytes.substack.com/ ) one day soon.

NOTE

kailaix

# solve 1D wave equation with ABC
using PyPlot
using Plots
using LinearAlgebra


N = 100
x = LinRange(-1.,1.,N+1)
Δx = x[2]-x[1]
NT = 200

rmnshelest

https://abooij.github.io/wiwikwlhott/

• univalence axiom
• higher inductive types

"Both ideas are impossible to capture directly in classical set-theoretic foundations, but when combined in homotopy type theory, they permit an entirely new kind of "logic of homotopy types"." p13

invariance, homotopy

univalence foundations

jw3126

# https://math.stackexchange.com/questions/1049788/haar-measure-of-an-angle-distance-ball-in-so3

using StatsBase
using LinearAlgebra
using StatPlots
using Plots
using Rotations

function sample_angles(N)
    map(1:N) do _

ArdWar

clear all; clc
% SHELL CONSTANTS %

C = 0.5561613; % PENETRATION
a = 9.81; % GRAVITY
T_0 = 288; % TEMPERATURE AT SEA LEVEL
L = 0.0065; % TEMPERATURE LAPSE RATE
p_0 = 101325; % PRESSURE AT SEA LEVEL
R = 8.31447; % UNIV GAS CONSTANT
M = 0.0289644; % MOLAR MASS OF AIR

delta2323

#!/usr/bin/env python                                                                                                                                                                                              

import numpy
import six
from matplotlib import pyplot


seed = 0
N = 100
M = 10

StuartGordonReid

def approximate_entropy(self, bin_data: str, pattern_length=10):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    As with the Serial test of Section 2.11, the focus of this test is the frequency of all possible overlapping
    m-bit patterns across the entire sequence. The purpose of the test is to compare the frequency of overlapping
    blocks of two consecutive/adjacent lengths (m and m+1) against the expected result for a random sequence.

    :param bin_data: a binary string

StuartGordonReid

class BinaryMatrix:
    def __init__(self, matrix, rows, cols):
        """
        This class contains the algorithm specified in the NIST suite for computing the **binary rank** of a matrix.
        :param matrix: the matrix we want to compute the rank for
        :param rows: the number of rows
        :param cols: the number of columns
        :return: a BinaryMatrix object
        """
        self.M = rows