{ "metadata": { "name": "", "signature": "sha256:9eb18cf1c093aa357e52cdcbd03acecabe8b141cbacff6f81576f3e00f1cc3d6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "

Electron Spin

\n", "
\n", "

\n", "This notebook goes with section 6 of the notes. It is intended to\n", "demonstrate two different types of electron spin problems.\n", "

\n", "(1) We are given an\n", "electron in some superposition of the x, y, or z spin states.\n", "Our job is to find out the probabilities for getting a + or - spin\n", "for some specified measurement. The measurement basis specified may\n", "or may not be the same one in which the original spin was given.\n", "

\n", "(2) We are given an electron whose spin is in some arbitrary\n", "physical direction. Then we are asked to find the probabilities\n", "of + or - for a measurement done in some other arbitrary direction.\n", "This is a more complicated problem, because we first have to\n", "calculate the \"mathematical model\" spin states that represent the\n", "physical directions given.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Contents

\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "

^ Setup Cell

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%autosave 0\n", "%matplotlib inline\n", "import sglib\n", "reload(sglib)\n", "from sglib import *\n", "from sympy import sqrt, cos, sin, pi" ], "language": "python", "metadata": {}, "outputs": [ { "javascript": [ "IPython.notebook.set_autosave_interval(0)" ], "metadata": {}, "output_type": "display_data" }, { "output_type": "stream", "stream": "stdout", "text": [ "Autosave disabled\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "

^ Superposition Spin Problem

\n", "

\n", "We are given an electron in the state $\\psi$ which is some superposition\n", "in one of the three bases x, y, or z.\n", "

\n", "We are then asked to calculate the probabilities of measuring a + or -\n", "spin in one of those three same bases. The given basis and the target\n", "basis may or may not be the same.\n", "

\n", "You can edit \"psi\" and \"meas_basis\" in the following cell to work\n", "the problems you're interested in. And, in the following cell, the\n", "problems from section 6.1 have been done." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Define the given state (any superposition in x,y, or z)\n", "psi = frac(1,2)*ket('+z') + frac(sqrt(3),2)*ket('-z')\n", "#psi = ket('-z')\n", "# Select the basis in which to do the measurement ( 'x', 'y', or 'z')\n", "meas_basis = 'x'\n", "superposition_spin_problem(psi, meas_basis)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "


" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 1: Write out the state in the z basis" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "First we need to write out the given state as a column vector." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "We do this simply by substituting in the values of the basis" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "states given, multiplying them by the given probability moduli," ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "and adding it all together. Note that the resulting column" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "vector will automatically be in the z basis." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\psi_z=\\left[\\begin{matrix}\\frac{1}{2}\\\\\\frac{\\sqrt{3}}{2}\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 2: Do a change of basis" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Now we need to do a change of basis into the specified target" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "basis (in this case, the x basis). We find the two components " ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "of $\\psi$ in the x basis by projecting $\\psi$ on to the two" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "x basis vectors. Suppose we call the two components of $\\psi$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "in the x basis by the names $\\alpha$ and $\\beta$. Then:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\alpha = \\langle +x| \\psi \\rangle = \\frac{1}{\\sqrt{2}}\\left[\\begin{matrix}1 & 1\\end{matrix}\\right]\\left[\\begin{matrix}\\frac{1}{2}\\\\\\frac{\\sqrt{3}}{2}\\end{matrix}\\right] = \\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\beta = \\langle -x| \\psi \\rangle = \\frac{1}{\\sqrt{2}}\\left[\\begin{matrix}1 & -1\\end{matrix}\\right]\\left[\\begin{matrix}\\frac{1}{2}\\\\\\frac{\\sqrt{3}}{2}\\end{matrix}\\right] = - \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "And the column vector in the x basis is: $\\left[\\begin{matrix}\\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}\\\\- \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 3: Calculate the probabilities" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "We can write this in \"ket format\" in the x basis by recognizing" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "that the components of the vector are the same as thing as the" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "probability moduli." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\psi_x = \\left(\\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}\\right)|+x\\rangle+\\left(- \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}\\right)|-x\\rangle$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "And then calculate the probabilties:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+) = \\left|\\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}\\right|^2 = \\left(\\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}\\right)\\left(\\frac{\\sqrt{2}}{4} + \\frac{\\sqrt{6}}{4}\\right) = \\frac{\\sqrt{3}}{4} + \\frac{1}{2}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(-) = \\left|- \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}\\right|^2 = \\left(- \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}\\right)\\left(- \\frac{\\sqrt{6}}{4} + \\frac{\\sqrt{2}}{4}\\right) = - \\frac{\\sqrt{3}}{4} + \\frac{1}{2}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Section 6.1 spin problems

\n", "

\n", "Here are the section 6.1 problems as further examples. These have\n", "the \"quiet\" flag turned on, so they just print the answers. You can\n", "turn that off, if you want to see the details. Note: It looks like\n", "perhaps the answer to problem 8, as given in the notes, may be wrong." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Print('6.1-1: we want +')\n", "psi = frac(1,sqrt(2))*ket('+z') + frac(1,sqrt(2))*ket('-z')\n", "meas_basis = 'z'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-2: we want +')\n", "psi = frac(1,sqrt(2))*ket('+z') + frac(1,sqrt(2))*ket('-z')\n", "meas_basis = 'x'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-3: we want +')\n", "psi = -frac(4,5)*ket('+x') - frac(3,5)*ket('-x')\n", "meas_basis = 'x'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-4: we want +')\n", "psi = frac(3,sqrt(10))*ket('+y') + frac(1,sqrt(10))*ket('-y')\n", "meas_basis = 'z'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-5: we want -')\n", "psi = -frac(5+4*i,sqrt(51))*ket('+x') + frac(3+i,sqrt(51))*ket('-x')\n", "meas_basis = 'z'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-6: we want -')\n", "psi = -frac(1+2*i,sqrt(10))*ket('+z') + frac(1+2*i,sqrt(10))*ket('-z')\n", "meas_basis = 'y'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-7: we want -')\n", "psi = frac(3*i,sqrt(35))*ket('+z') + frac(1+5*i,sqrt(35))*ket('-z')\n", "meas_basis = 'z'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)\n", "\n", "Print('6.1-8: we want -')\n", "psi = frac(4+3*i,5*sqrt(2))*ket('+z') + frac(3+4*i,5*sqrt(2))*ket('-z')\n", "meas_basis = 'x'\n", "superposition_spin_problem(psi, meas_basis, quiet=True)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "6.1-1: we want +" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{1}{2},\\;\\;\\;\\;P(-)=\\frac{1}{2}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-2: we want +" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=1,\\;\\;\\;\\;P(-)=0$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-3: we want +" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{16}{25},\\;\\;\\;\\;P(-)=\\frac{9}{25}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-4: we want +" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{4}{5},\\;\\;\\;\\;P(-)=\\frac{1}{5}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-5: we want -" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{13}{102},\\;\\;\\;\\;P(-)=\\frac{89}{102}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-6: we want -" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{1}{2},\\;\\;\\;\\;P(-)=\\frac{1}{2}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-7: we want -" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{9}{35},\\;\\;\\;\\;P(-)=\\frac{26}{35}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "6.1-8: we want -" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(+)=\\frac{49}{50},\\;\\;\\;\\;P(-)=\\frac{1}{50}$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "


\n", "\n", "

^ Arbitrary Direction Spin Problem

\n", "

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "s_2 = col(1/sqrt(2), 0, 1/sqrt(2)) # This is p45 in the Bell notebook\n", "# = col(0, 1/sqrt(2), 1/sqrt(2))\n", "# = col(1/sqrt(2), 1/sqrt(2), 0)\n", "# = col(0,0,1)\n", "s_1 = col(1,0,0)\n", "# = col(0,1,0)\n", "\n", "arbitrary_spin_problem(s_1, s_2, exact=False, ndigs=3, draw_box=True)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "


" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "The spin vectors are:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$s_1 = \\left[\\begin{matrix}1.0\\\\0\\\\0\\end{matrix}\\right],\\;\\; s_2=\\left[\\begin{matrix}0.707\\\\0\\\\0.707\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Given an electron in the state $|+s_1\\rangle$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "We want the probabilities of measuring $|+s_2\\rangle$ or $|-s_2\\rangle$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 1:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Calculate the measurement operators for both spin vectors" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "using the formula: $O = s_x\\sigma_x + s_y\\sigma_y + s_z\\sigma_z$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Calculating the measurment operator for $s_1$ ..." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$O_1 = 1.0 \\left[\\begin{matrix}0 & 1.0\\\\1.0 & 0\\end{matrix}\\right] + 0 \\left[\\begin{matrix}0 & - 1.0 i\\\\1.0 i & 0\\end{matrix}\\right] + 0 \\left[\\begin{matrix}1.0 & 0\\\\0 & -1.0\\end{matrix}\\right] = \\left[\\begin{matrix}0 & 1.0\\\\1.0 & 0\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Calculating the measurment operator for $s_2$ ..." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$O_2 = 0.707 \\left[\\begin{matrix}0 & 1.0\\\\1.0 & 0\\end{matrix}\\right] + 0 \\left[\\begin{matrix}0 & - 1.0 i\\\\1.0 i & 0\\end{matrix}\\right] + 0.707 \\left[\\begin{matrix}1.0 & 0\\\\0 & -1.0\\end{matrix}\\right] = \\left[\\begin{matrix}0.707 & 0.707\\\\0.707 & -0.707\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 2:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "The computer will find the eigenvalues and eigenvectors" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Eigenvalues for $O_1$ are: $-1.0$ and $1.0$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Eigenvectors for $O_1$ are: $\\left[\\begin{matrix}0.707\\\\-0.707\\end{matrix}\\right]$ and $\\left[\\begin{matrix}0.707\\\\0.707\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Eigenvalues for $O_2$ are: $-1.0$ and $1.0$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Eigenvectors for $O_2$ are: $\\left[\\begin{matrix}-0.383\\\\0.924\\end{matrix}\\right]$ and $\\left[\\begin{matrix}0.924\\\\0.383\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "The eigenvalues tell us which vectors represent the \"plus\"" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "states and which represent the \"minus\" states" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$|+s_1\\rangle=\\left[\\begin{matrix}0.707\\\\0.707\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$|+s_2\\rangle=\\left[\\begin{matrix}0.924\\\\0.383\\end{matrix}\\right],\\;\\; |-s_2\\rangle=\\left[\\begin{matrix}-0.383\\\\0.924\\end{matrix}\\right]$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 3:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Now that we know what the $s_2$ state vectors are, we want to" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "project our given state, $|+s_1\\rangle$, onto to the $s_2$ basis. This will" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "give us an expression such as: $\\alpha |+s_2\\rangle + \\beta |-s_2\\rangle$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "from which we can calculate the probabilities." ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
The projection onto $s_2$ goes like this:
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\alpha = \\langle +s_2 | +s_1 \\rangle$$ = \\left[\\begin{matrix}0.924 & 0.383\\end{matrix}\\right] \\left[\\begin{matrix}0.707\\\\0.707\\end{matrix}\\right]$$= 0.924$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\beta = \\langle -s_2 | +s_1 \\rangle$$ = \\left[\\begin{matrix}-0.383 & 0.924\\end{matrix}\\right] \\left[\\begin{matrix}0.707\\\\0.707\\end{matrix}\\right]$$= 0.383$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
The state written in the $s_2$ basis is:
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
     $(0.924)|+s_2\\rangle + (0.383)|-s_2\\rangle$
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Step 4: Calculate the probabilities" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(|+s_2\\rangle)=|\\alpha|^2=\\alpha\\alpha^*$$= (0.924)(0.924)$$=0.854$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$P(|-s_2\\rangle)=|\\beta|^2=\\beta\\beta^*$$= (0.383)(0.383)$$=0.146$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "Additional Note:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "The angle between $s_1$ and $s_2$ is: $0.785$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "The probabilities should be equivalent to:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "$\\cos^2(\\theta)=0.854$ and $\\sin^2(\\theta)=0.146$" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "
" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "html": [ "And finally, here's a picture of the spin vectors:" ], "metadata": {}, "output_type": "display_data", "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAG+CAYAAADsjWHpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRiH4AABG+R+HW6mPhB/1/gAAAABJRU5ErkJggg==\n", "text": [ "" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 } ], "metadata": {} } ] }