{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An example of how to run GST on a 2-qubit system\n", "This example gives an overview of the typical steps used to perform an end-to-end (i.e. experimental-data-to-report) Gate Set Tomography analysis on a 2-qubit system. The steps are very similar to the single-qubit case described in the tutorials, but we thought 2Q-GST is an important enough topic to deserve a separate example. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import print_function\n", "import pygsti" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: Construct the desired 2-qubit gateset\n", "Since the purpose of this example is to show how to *run* 2Q-GST, we'll just use a built-in \"standard\" 2-qubit gate set. (Another example covers how to create a custom 2-qubit gate set.)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pygsti.construction import std2Q_XYICNOT\n", "gs_target = std2Q_XYICNOT.gs_target.copy() #copying is good practice so we don't inadvertetly mess up std2Q_XYCNOT.gs_target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Obtain lists of fiducial and germ gate sequences\n", "These are the building blocks of the gate sequences performed in the experiment. Typically, these lists are either provided by pyGSTi because you're using a \"standard\" gate set (as we are here), or computed using the \"fiducial selection\" and \"germ selection\" algorithms which are a part of pyGSTi and covered in the tutorials. Since 2Q-GST with the 71 germs of the complete set would take a while, we'll also create a couple of small germ sets to demonstrate 2Q-GST more quickly (because we know you have important stuff to do)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "prep_fiducials = std2Q_XYICNOT.prepStrs\n", "effect_fiducials = std2Q_XYICNOT.effectStrs" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "germs4 = pygsti.construction.gatestring_list(\n", " [ ('Gix',), ('Giy',), ('Gxi',), ('Gyi',) ] )\n", "\n", "germs11 = pygsti.construction.gatestring_list(\n", " [ ('Gix',), ('Giy',), ('Gxi',), ('Gyi',), ('Gcnot',), ('Gxi','Gyi'), ('Gix','Giy'),\n", " ('Gix','Gcnot'), ('Gxi','Gcnot'), ('Giy','Gcnot'), ('Gyi','Gcnot') ] )\n", "\n", "germs71 = std2Q_XYICNOT.germs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Data generation\n", "Now that fiducial and germ strings have been found, we can generate the list of experiments needed to run GST, just like in the 1-qubit case. As an additional input we'll need a list of lengths indicating the maximum length strings to use on each successive GST iteration." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#A list of maximum lengths for each GST iteration - typically powers of 2 up to\n", "# the longest experiment you can glean information from. Here we just pick 2 so things run quickly.\n", "maxLengths = [1,2] # 4,16,32...\n", "\n", "#Create a list of GST experiments for this gateset, with\n", "#the specified fiducials, germs, and maximum lengths. We use\n", "#\"germs4\" here so that the tutorial runs quickly; really, you'd\n", "#want to use germs71!\n", "listOfExperiments = pygsti.construction.make_lsgst_experiment_list(gs_target.gates.keys(), prep_fiducials,\n", " effect_fiducials, germs4, maxLengths)\n", "\n", "#Create an empty dataset file, which stores the list of experiments\n", "# and zerod-out columns where data should be inserted. Note the use of the SPAM\n", "# labels in the \"Columns\" header line.\n", "pygsti.io.write_empty_dataset(\"example_files/My2QDataTemplate.txt\", listOfExperiments,\n", " \"## Columns = 00 count, 01 count, 10 count, 11 count\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#Generate some \"fake\" (simulated) data based on a depolarized version of the target gateset\n", "gs_datagen = gs_target.depolarize(gate_noise=0.1, spam_noise=0.001)\n", "ds = pygsti.construction.generate_fake_data(gs_datagen, listOfExperiments, nSamples=1000,\n", " sampleError=\"multinomial\", seed=2016)\n", "\n", "#if you have a dataset file with real data in it, load it using something like:\n", "#ds = pygsti.io.load_dataset(\"mydir/My2QDataset.txt\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Run GST using `do_long_sequence_gst`\n", "Just like for 1-qubit GST, we call the driver routine `do_long_sequence_gst` to compute the GST estimates. Usually for two qubits this could take a long time (hours on a single cpu) based on the number of gate sequences used, and running on multiple processors is a good idea (see the MPI example). However, since we chose an incomplete set of only 4 germs and set our maximum max-length to 2, this will run fairly quickly (~10min).\n", "\n", "Some notes about the options/arguments to `do_long_sequence_gst` that are particularly relevant to 2-qubit GST:\n", " - `memoryLimit` gives an estimate of how much memory is available to use on your system (in bytes). This is currently *not* a hard limit, and pyGSTi may require slightly more memory than this \"limit\". So you'll need to be conservative in the value you place here: if your machine has 10GB of RAM, set this to 6 or 8 GB initially and increase it as you see how much memory is actually used using a separate OS performance monitor tool. If you're running on multiple processors, this should be the memory available *per processor*.\n", " - `verbosity` tells the routine how much detail to print to stdout. If you don't mind waiting a while without getting any output, you can leave this at its default value (2). If you can't standing wondering whether GST is still running or has locked up, set this to 3.\n", " - `advancedOptions` is a dictionary that accepts various \"advanced\" settings that aren't typically needed. While we don't require its use below, the `depolarizeStart` key of this dictionary may be useful in certain cases: it gives an amount (in [0,1]) to depolarize the (LGST) estimate that is used as the initial guess for long-sequence GST. In practice, we find that, sometime, in the larger 2-qubit Hilbert space, the LGST estimate may be so poor as to adversely affect the subsequent long-sequence GST (e.g. very slow convergence). Depolarizing the LGST estimate can remedy this. If you're unsure what to put here, either don't specify `depolarizeLGST` at all (the same as using 0.0), or just use 0.1, i.e. `advancedOptions={ 'depolarizeStart' : 0.1 }`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--- Gate Sequence Creation ---\n", " 1317 sequences created\n", " Dataset has 1317 entries: 1317 utilized, 0 requested sequences were missing\n", "--- LGST ---\n", " Singular values of I_tilde (truncating to first 16 of 16) = \n", " 6.7502828285173155\n", " 2.3518957405180436\n", " 2.318639069417392\n", " 1.2302334842041527\n", " 1.2117463743117198\n", " 1.1873969447789428\n", " 0.8897042679854841\n", " 0.8169918501235112\n", " 0.5305300820082018\n", " 0.5155269364101752\n", " 0.3676760156532707\n", " 0.3517041657230517\n", " 0.30932109560940213\n", " 0.2334365962706313\n", " 0.22377281697587817\n", " 0.14850701015514287\n", " \n", " Singular values of target I_tilde (truncating to first 16 of 16) = \n", " 6.868027641505519\n", " 3.202537446873216\n", " 3.202537446873215\n", " 1.7692369322250323\n", " 1.7692369322250308\n", " 1.7320508075688799\n", " 1.2340048586337\n", " 1.2247448713915883\n", " 0.7071067811865485\n", " 0.7071067811865481\n", " 0.5000000000000001\n", " 0.49371439251332727\n", " 0.49371439251332666\n", " 0.3461223449171741\n", " 0.34612234491717386\n", " 0.2396420755723003\n", " \n", " Resulting gate set:\n", " \n", " rho0 = FullyParameterizedSPAMVec with dimension 16\n", " 0.50 0 0 0.50 0 0 0 0 0 0 0 0 0.50 0 0 0.50\n", " \n", " \n", " Mdefault = UnconstrainedPOVM with effect vectors:\n", " 00: FullyParameterizedSPAMVec with dimension 16\n", " 0.60-0.06 0.07 0.45-0.04 0.03-0.02-0.04 0.07 0 0.02 0.05 0.45-0.07 0.08 0.49\n", " \n", " 01: FullyParameterizedSPAMVec with dimension 16\n", " 0.50 0.06-0.06-0.45-0.03-0.06-0.01 0.01 0 0.02-0.09-0.02 0.36 0.05-0.07-0.41\n", " \n", " 10: FullyParameterizedSPAMVec with dimension 16\n", " 0.49-0.02 0.05 0.35 0.05 0 0.04 0.06-0.05-0.02 0.02-0.07-0.45 0.03-0.06-0.40\n", " \n", " 11: FullyParameterizedSPAMVec with dimension 16\n", " 0.41 0.02-0.06-0.36 0.03 0.03-0.01-0.04-0.03 0 0.05 0.04-0.37 0 0.06 0.31\n", " \n", " \n", " \n", " Gii = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.02 0.87 0.05 0-0.04 0.05 0.02-0.01-0.02 0.05-0.07 0 0 0.02-0.03 0.02\n", " 0.02 0.04 0.90 0 0.04-0.12 0.05-0.04 0 0.06 0.02 0.01 0.02-0.04 0.07 0.02\n", " 0-0.02-0.02 0.91-0.02 0.04 0.03-0.02 0.02-0.04-0.03 0 0-0.02-0.03-0.01\n", " -0.02 0-0.01 0 0.92 0.02 0.03-0.02-0.05 0.05-0.11 0 0.03-0.05 0.03-0.02\n", " -0.02-0.10 0.05 0 0.04 0.91 0.13 0-0.03 0 0.08-0.04-0.02 0-0.03-0.04\n", " -0.04-0.04 0.03 0.03 0.04 0.07 0.88-0.04-0.01-0.16 0.07 0.05 0 0.09 0.04 0\n", " 0 0.05-0.03-0.02-0.03-0.08 0.05 0.96-0.05 0.12 0-0.11-0.08 0.10-0.10 0.03\n", " -0.05 0.07-0.08 0.02 0.05-0.05 0.09-0.05 0.83 0.13-0.07 0.04 0 0.07 0.01 0.02\n", " -0.07 0.10-0.07-0.07 0.02-0.06 0 0.12 0.02 0.78 0.13-0.11-0.03 0.12-0.02-0.03\n", " -0.01 0.10 0.06-0.04-0.02-0.08-0.13 0.04 0 0.06 0.85 0 0-0.12 0.05 0\n", " 0.04-0.04 0.06-0.05-0.05 0.04-0.02 0.05 0.07-0.22 0.05 0.83 0-0.04-0.01 0.06\n", " 0 0 0 0-0.02-0.01-0.05 0 0.01-0.04 0.04-0.06 0.90 0 0 0\n", " -0.01 0.02-0.04 0 0.06-0.12 0.06 0.04 0.05-0.13 0 0.02 0.05 0.83 0.11-0.04\n", " 0.04-0.06 0.05-0.02-0.02 0.08-0.04 0.03 0.08-0.09 0.10-0.04 0 0 0.89 0\n", " 0-0.04-0.03 0 0.03 0 0 0.01-0.07 0-0.12 0.03 0 0-0.01 0.90\n", " \n", " \n", " Gix = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.02 0.89 0.02-0.02-0.02-0.01-0.02 0.02-0.02 0.08-0.02 0.02 0 0.02 0 0\n", " -0.08 0.10-0.08-0.92 0.04-0.08 0.04-0.04 0-0.02 0 0 0-0.02 0 0\n", " -0.10 0.10 0.90 0.10-0.03 0.05-0.03 0.03 0.01-0.01 0.01-0.01 0.02-0.04 0.02-0.02\n", " -0.01 0.03-0.01 0.01 0.92-0.04-0.08 0.08-0.02 0.08-0.02 0.02 0 0 0 0\n", " 0 0 0 0 0.01 0.78 0.01-0.01 0.03-0.01 0.03-0.03 0 0 0 0\n", " 0.01-0.05 0.01-0.01-0.13 0.18-0.13-0.87 0 0 0 0 0.02 0.01 0.02-0.02\n", " 0 0 0 0-0.10 0.06 0.90 0.10 0-0.04 0 0-0.02 0.02-0.02 0.02\n", " 0 0.01 0 0 0.02-0.01 0.02-0.02 0.89 0.05-0.11 0.11 0.02 0 0.02-0.02\n", " -0.06 0.02-0.06 0.06 0.06 0.11 0.06-0.06 0.03 0.81 0.03-0.03-0.01 0.06-0.01 0.01\n", " 0.02-0.04 0.02-0.02-0.09 0.04-0.09 0.09-0.11 0.08-0.11-0.89 0.02-0.04 0.02-0.02\n", " -0.01 0.05-0.01 0.01 0.04 0.03 0.04-0.04-0.09 0.15 0.91 0.09 0-0.06 0 0\n", " -0.02 0-0.02 0.02-0.01 0.01-0.01 0.01 0 0 0 0 0.91 0-0.09 0.09\n", " -0.03 0.03-0.03 0.03 0.04-0.12 0.04-0.04-0.06 0.10-0.06 0.06 0.04 0.85 0.04-0.04\n", " 0.03-0.04 0.03-0.03-0.13 0.18-0.13 0.13 0.05 0 0.05-0.05-0.09 0.14-0.09-0.91\n", " -0.02 0.02-0.02 0.02 0.05-0.13 0.05-0.05-0.04 0.01-0.04 0.04-0.09 0.12 0.91 0.09\n", " \n", " \n", " Giy = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.12-0.13 0.08 0.88-0.03 0.05 0.04 0.03-0.04 0-0.12 0.04-0.01 0 0 0.01\n", " 0.03 0 0.94-0.03 0.05 0.08 0.03-0.05 0-0.06 0.06 0-0.02 0 0 0.02\n", " -0.09-0.93-0.10 0.09-0.05 0.04-0.02 0.05 0 0.01-0.05 0 0.01 0.01 0-0.01\n", " -0.02 0.02-0.06 0.02 0.93 0.06 0.04 0.07-0.08 0.07-0.15 0.08 0.04 0 0.04-0.04\n", " 0.03-0.04 0.07-0.03 0.07-0.06 0.11 0.93 0.09-0.09 0.16-0.09-0.01 0.02 0 0.01\n", " 0 0 0.09 0 0 0.08 0.80 0 0-0.04 0.17 0 0-0.03 0.02 0\n", " 0-0.05-0.04 0-0.12-0.83-0.09 0.12-0.03-0.07-0.02 0.03 0-0.08-0.06 0\n", " 0 0.01-0.05 0 0-0.02 0.05 0 0.87 0.07-0.07 0.13 0.02-0.02 0.01-0.02\n", " -0.05 0-0.08 0.05 0 0 0.07 0 0.10-0.09 0.14 0.90-0.03 0.05-0.03 0.03\n", " 0.02-0.04 0.09-0.02-0.01-0.02-0.08 0.01 0.05 0.03 1.01-0.05 0.02 0.04 0.01-0.02\n", " -0.04 0 0 0.04-0.02 0.09 0.02 0.02-0.05-0.92-0.08 0.05 0.02-0.03 0.03-0.02\n", " 0-0.01 0 0-0.01 0.02 0 0.01 0.02 0 0.02-0.02 0.90 0.11 0 0.10\n", " -0.03-0.02 0 0.03 0.06 0.03 0.04-0.06 0.03 0 0.09-0.03 0.15-0.11 0.11 0.85\n", " 0.02-0.02 0-0.02-0.06-0.01-0.12 0.06 0.10 0.04 0.11-0.10 0.03-0.09 0.93-0.03\n", " -0.01 0 0 0.01 0.04-0.02 0.03-0.04-0.03-0.09-0.02 0.03-0.08-0.92-0.10 0.08\n", " \n", " \n", " Gxi = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.02 0.86 0.02 0-0.04 0.05 0 0.02 0.02-0.12 0.11 0 0 0.09 0.02-0.02\n", " 0-0.05 0.93 0 0.06-0.06 0.04-0.03 0-0.04-0.11 0.02 0.02-0.02 0.07-0.03\n", " -0.01 0.03-0.03 0.92-0.03 0.01 0 0 0-0.02 0.03-0.10 0 0.04 0.03 0.10\n", " 0 0 0 0 0.87-0.01 0 0 0.03-0.05 0.06-0.02 0-0.03 0.05 0\n", " -0.01-0.06-0.03 0-0.06 0.94 0 0.02 0.04-0.11 0.08-0.06 0 0-0.03 0.02\n", " 0-0.05 0.02 0 0.03 0 0.94-0.03 0.03-0.08 0-0.03 0.02 0-0.04-0.02\n", " 0.02 0-0.04 0-0.04 0-0.03 0.90-0.02 0.04-0.11 0.05 0-0.02-0.03-0.03\n", " -0.10 0.01-0.01 0 0.10 0.02 0.04 0.01-0.06-0.07 0.03-0.04-0.88-0.03 0.05-0.02\n", " 0.01-0.12-0.04 0-0.02 0.19 0.07 0.07 0.04-0.14-0.07-0.02-0.02-0.83-0.02 0\n", " -0.02 0.02-0.09 0.06 0.01-0.06-0.09-0.08 0-0.07-0.14 0.03-0.08 0.09-0.97 0.05\n", " 0-0.03 0-0.09 0.09 0.02 0.16 0.08-0.06-0.01 0.01-0.03 0 0.01 0.04-0.91\n", " -0.09-0.01 0 0 0.07-0.03 0.01 0 0.90 0 0 0 0.07 0.04-0.02 0.03\n", " -0.01-0.08 0.05 0.02 0.05-0.03-0.02-0.08 0.01 0.86 0 0-0.03 0.08-0.03 0.02\n", " 0 0-0.14 0 0 0.06 0.07 0.02-0.04 0.02 0.85 0.05-0.06 0.09-0.02 0.06\n", " 0.02-0.03 0-0.10-0.01-0.05-0.01 0.07 0.03 0.02 0 0.90 0.03-0.02 0.01 0.04\n", " \n", " \n", " Gyi = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.02 0.88-0.01-0.01 0.04 0.06-0.01 0 0 0.04-0.02-0.03-0.04 0.12 0.02 0.03\n", " 0.02 0 0.93 0.01-0.02 0.02 0.07-0.01 0 0.07 0.02 0.02 0.03-0.06 0.06-0.06\n", " 0-0.04 0 0.91 0 0.04 0 0.13 0.02-0.03 0 0-0.02 0.01 0.01 0.10\n", " 0.08 0.03 0 0-0.15 0.05-0.08 0 0.05 0.07-0.12 0 0.92-0.03 0 0\n", " 0 0.05 0.04 0 0.03-0.14 0.05 0.04 0-0.02 0.09 0.04 0.02 0.83 0.13-0.02\n", " 0 0.06 0.15 0.03-0.02-0.03-0.09-0.08-0.04 0.05 0.14 0.03 0.02-0.06 0.88-0.04\n", " -0.01-0.02-0.03 0.08 0.06-0.02 0.02-0.12-0.06 0.03 0.05 0.02 0.03 0 0.11 0.91\n", " -0.01-0.02 0 0.02 0.01 0 0 0 0.86 0.04-0.07 0.03 0 0.02 0-0.01\n", " -0.04-0.01 0-0.02-0.05 0.10-0.19 0.04 0 0.75 0.03-0.03 0 0 0 0.05\n", " 0.01 0.03 0.09 0.03-0.03 0.04-0.04-0.02 0.08-0.08 0.96 0-0.04-0.05-0.06 0\n", " -0.01 0 0 0 0 0.08-0.01 0.02 0.02-0.09 0.02 0.91 0.01 0.06 0.04 0\n", " -0.09-0.02 0-0.01-0.91 0 0 0-0.08 0-0.03-0.04 0.09-0.06 0 0.02\n", " -0.04-0.10 0 0-0.06-0.81 0.05 0.04 0.01-0.13 0.04-0.03 0.05-0.02-0.01-0.01\n", " 0 0.05-0.05 0.01 0.04-0.03-0.86-0.11 0.03-0.03 0.06-0.01-0.03 0.10 0.09 0.02\n", " -0.02 0.04-0.04-0.09 0-0.08 0.03-0.93-0.05 0.10 0.03-0.08 0.01-0.04 0 0.09\n", " \n", " \n", " Gcnot = \n", " FullyParameterizedGate with shape (16, 16)\n", " 1.00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", " 0.02 0.87 0 0.02-0.01 0.02-0.03 0.02 0.03-0.02-0.03 0.07 0 0.01 0.04 0\n", " 0.04-0.05 0.06 0.02-0.06 0.05-0.15 0.03 0.07-0.05 0.19 0 0 0.03 0.94-0.02\n", " -0.11 0.09 0-0.02 0.01 0-0.01-0.06 0-0.01 0 0.10 0.11-0.12-0.04 0.92\n", " -0.01 0.03 0.06 0-0.03 0.91-0.06 0.10 0.03 0.03 0.06-0.03 0.01-0.03-0.04 0\n", " -0.04 0.01-0.04 0 0.86-0.07-0.06 0.06-0.01-0.06 0.05 0.10 0-0.06-0.01-0.03\n", " 0 0 0.10-0.04 0 0-0.01-0.01 0.10-0.07 0.13 0.79 0 0 0.07 0.02\n", " 0.02-0.04-0.06 0.01 0.05-0.15 0.05-0.02 0.05-0.13-0.81-0.06-0.08 0.03-0.02 0.02\n", " -0.03 0.04-0.01 0 0.04 0.04-0.01 0.05 0.02 0.94 0.03-0.03 0.03-0.03 0 0.02\n", " -0.04 0.03-0.02-0.01 0.10 0.02 0.04-0.10 0.86 0.06-0.04 0.12-0.03 0.03-0.06-0.07\n", " 0 0.05-0.02-0.03-0.12-0.02-0.07-0.87-0.03-0.02 0-0.02 0-0.08 0.06-0.02\n", " 0 0.02 0.05 0.05 0.10-0.10 0.88 0.12 0.06 0.04 0.04 0.08-0.02-0.05 0.03 0\n", " 0 0 0-0.07-0.12 0.10-0.04-0.04 0.07-0.09 0.02 0 0.91-0.02-0.01 0.09\n", " 0-0.02 0.10-0.06 0.02-0.05-0.03 0.06-0.10 0.04 0.02-0.06 0.01 0.84-0.04 0.03\n", " 0 0.06 0.90 0.03 0.04-0.03-0.09-0.06-0.02 0.12 0.01 0.09 0.06-0.10 0.04 0\n", " 0.10-0.13 0 0.92-0.04 0.03-0.11 0.02 0-0.06-0.13 0-0.10 0.13 0-0.01\n", " \n", " \n", " \n", " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--- Iterative MLGST: Iter 1 of 2 907 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Memory limit = 3.00GB\n", " Cur, Persist, Gather = 0.14, 0.04, 0.30 GB\n", " Evaltree generation (default) w/mem limit = 2.52GB\n", " mem(1 subtrees, 1,1 param-grps, 1 proc-grps) in 0s = 2.80GB (2.80GB fc)\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 1616 params (taken as 2 param groups of ~808 params).\n", " Memory estimate = 1.40GB (cache=907, wrtLen1=808, wrtLen2=1616, subsPerProc=1).\n", " --- Outer Iter 0: norm_f = 5833.47, mu=0, |J|=9795.49\n", " --- Outer Iter 1: norm_f = 1952.43, mu=1933.11, |J|=9726.98\n", " --- Outer Iter 2: norm_f = 1642.96, mu=644.37, |J|=9666.38\n", " --- Outer Iter 3: norm_f = 1555.63, mu=214.79, |J|=9660.03\n", " --- Outer Iter 4: norm_f = 1517.27, mu=71.5967, |J|=9672.62\n", " --- Outer Iter 5: norm_f = 1501.95, mu=23.8656, |J|=9693.31\n", " --- Outer Iter 6: norm_f = 1497.5, mu=7.95519, |J|=9715.91\n", " --- Outer Iter 7: norm_f = 1496.41, mu=2.65173, |J|=9730.51\n", " --- Outer Iter 8: norm_f = 1496.21, mu=0.88391, |J|=9737.72\n", " --- Outer Iter 9: norm_f = 1496.19, mu=0.294637, |J|=9740.22\n", " --- Outer Iter 10: norm_f = 1496.19, mu=0.0982122, |J|=9740.87\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Finding num_nongauge_params is too expensive: using total params.\n", " Sum of Chi^2 = 1496.19 (2720 data params - 1616 model params = expected mean of 1104; p-value = 2.40918e-14)\n", " Completed in 704.2s\n", " 2*Delta(log(L)) = 1501.17\n", " Iteration 1 took 704.3s\n", " \n", "--- Iterative MLGST: Iter 2 of 2 1317 gate strings ---: \n", " --- Minimum Chi^2 GST ---\n", " Memory limit = 3.00GB\n", " Cur, Persist, Gather = 0.25, 0.06, 0.29 GB\n", " Evaltree generation (default) w/mem limit = 2.40GB\n", " mem(1 subtrees, 1,1 param-grps, 1 proc-grps) in 1s = 4.06GB (4.06GB fc)\n", " Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n", " groups of ~1 procs each, to distribute over 1616 params (taken as 2 param groups of ~808 params).\n", " Memory estimate = 2.03GB (cache=1317, wrtLen1=808, wrtLen2=1616, subsPerProc=1).\n", " --- Outer Iter 0: norm_f = 4476.66, mu=0, |J|=11844\n", " --- Outer Iter 1: norm_f = 3365.67, mu=2583.99, |J|=11739.7\n", " --- Outer Iter 2: norm_f = 3021.89, mu=861.328, |J|=11712.6\n", " --- Outer Iter 3: norm_f = 2855.59, mu=287.109, |J|=11699.6\n", " --- Outer Iter 4: norm_f = 2784.85, mu=95.7032, |J|=11689\n", " --- Outer Iter 5: norm_f = 2765.13, mu=31.9011, |J|=11686.2\n", " --- Outer Iter 6: norm_f = 2761.34, mu=10.6337, |J|=11686.7\n", " --- Outer Iter 7: norm_f = 2760.97, mu=3.54456, |J|=11686.7\n", " --- Outer Iter 8: norm_f = 2760.95, mu=1.18152, |J|=11686.5\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Finding num_nongauge_params is too expensive: using total params.\n", " Sum of Chi^2 = 2760.95 (3950 data params - 1616 model params = expected mean of 2334; p-value = 1.67177e-09)\n", " Completed in 769.5s\n", " 2*Delta(log(L)) = 2771.33\n", " Iteration 2 took 769.6s\n", " \n", " Switching to ML objective (last iteration)\n", " --- MLGST ---\n", " Memory: limit = 3.00GB(cur, persist, gthr = 0.27, 0.06, 0.29 GB)\n", " --- Outer Iter 0: norm_f = 1385.67, mu=0, |J|=8271.12\n", " --- Outer Iter 1: norm_f = 1384.07, mu=1284.17, |J|=8283.99\n", " --- Outer Iter 2: norm_f = 1383.96, mu=428.057, |J|=8282.83\n", " --- Outer Iter 3: norm_f = 1383.91, mu=142.686, |J|=8282.29\n", " --- Outer Iter 4: norm_f = 1383.88, mu=47.5619, |J|=8282.08\n", " --- Outer Iter 5: norm_f = 1383.88, mu=15.854, |J|=8282.01\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Finding num_nongauge_params is too expensive: using total params.\n", " Maximum log(L) = 1383.88 below upper bound of -2.95403e+06\n", " 2*Delta(log(L)) = 2767.75 (3950 data params - 1616 model params = expected mean of 2334; p-value = 9.70072e-10)\n", " Completed in 312.4s\n", " 2*Delta(log(L)) = 2767.75\n", " Final MLGST took 312.4s\n", " \n", "Iterative MLGST Total Time: 1786.3s\n", " -- Adding Gauge Optimized (go0) --\n", "--- Re-optimizing logl after robust data scaling ---\n", " --- MLGST ---\n", " Memory: limit = 3.00GB(cur, persist, gthr = 0.25, 0.06, 0.29 GB)\n", " --- Outer Iter 0: norm_f = 1383.88, mu=0, |J|=8282.01\n", " Least squares message = Both actual and predicted relative reductions in the sum of squares are at most 1e-06\n", " Finding num_nongauge_params is too expensive: using total params.\n", " Maximum log(L) = 1383.88 below upper bound of -2.95403e+06\n", " 2*Delta(log(L)) = 2767.75 (3950 data params - 1616 model params = expected mean of 2334; p-value = 9.70072e-10)\n", " Completed in 37.9s\n", " -- Adding Gauge Optimized (go0) --\n", "Total time=0.507658 hours\n" ] } ], "source": [ "import time\n", "start = time.time()\n", "results = pygsti.do_long_sequence_gst(ds, gs_target, prep_fiducials, effect_fiducials, germs4,\n", " maxLengths, gaugeOptParams={'itemWeights': {'spam':0.1,'gates': 1.0}},\n", " memLimit=3*(1024)**3, verbosity=3 )\n", "end = time.time()\n", "print(\"Total time=%f hours\" % ((end - start) / 3600.0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: Create report(s) using the returned `Results` object\n", "The `Results` object returned from `do_long_sequence_gst` can be used to generate a \"general\" HTML report, just as in the 1-qubit case:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Creating workspace ***\n", "*** Generating switchboard ***\n", "*** Generating tables ***\n", " targetSpamBriefTable took 0.5372 seconds\n", " targetGatesBoxTable took 0.304077 seconds\n", " datasetOverviewTable took 0.045811 seconds\n", " bestGatesetSpamParametersTable took 0.000413 seconds\n", " bestGatesetSpamBriefTable took 0.348761 seconds\n", " bestGatesetSpamVsTargetTable took 1.261723 seconds\n", " bestGatesetGaugeOptParamsTable took 0.000318 seconds\n", " bestGatesetGatesBoxTable took 0.433634 seconds\n", " bestGatesetChoiEvalTable took 0.776777 seconds\n", " bestGatesetDecompTable took 7.105484 seconds\n", " bestGatesetEvalTable took 0.027012 seconds\n", " bestGermsEvalTable took 0.014787 seconds\n", " bestGatesetVsTargetTable took 0.137805 seconds\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/enielse/research/pyGSTi/packages/pygsti/extras/rb/theory.py:200: UserWarning:\n", "\n", "Output may be unreliable because the gateset is not approximately trace-preserving.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " bestGatesVsTargetTable_gv took 7.145125 seconds\n", " bestGatesVsTargetTable_gvgerms took 0.182492 seconds\n", " bestGatesVsTargetTable_gi took 0.102844 seconds\n", " bestGatesVsTargetTable_gigerms took 0.01115 seconds\n", " bestGatesVsTargetTable_sum took 7.114973 seconds\n", " bestGatesetErrGenBoxTable took 1.636278 seconds\n", " metadataTable took 0.000895 seconds\n", " stdoutBlock took 0.000938 seconds\n", " profilerTable took 0.000566 seconds\n", " softwareEnvTable took 0.030693 seconds\n", " exampleTable took 0.042623 seconds\n", " singleMetricTable_gv took 7.194592 seconds\n", " singleMetricTable_gi took 0.119974 seconds\n", " fiducialListTable took 0.000642 seconds\n", " prepStrListTable took 0.000417 seconds\n", " effectStrListTable took 0.000331 seconds\n", " colorBoxPlotKeyPlot took 0.054498 seconds\n", " germList2ColTable took 0.000224 seconds\n", " progressTable took 2.541248 seconds\n", "*** Generating plots ***\n", " gramBarPlot took 0.117085 seconds\n", " progressBarPlot took 1.754693 seconds\n", " progressBarPlot_sum took 0.000267 seconds\n", " finalFitComparePlot took 0.922525 seconds\n", " bestEstimateColorBoxPlot took 2.471977 seconds\n", " bestEstimateTVDColorBoxPlot took 2.047931 seconds\n", " bestEstimateColorScatterPlot took 2.87438 seconds\n", " bestEstimateColorHistogram took 2.368953 seconds\n", " progressTable_scl took 8.4e-05 seconds\n", " progressBarPlot_scl took 5.3e-05 seconds\n", " bestEstimateColorBoxPlot_scl took 8.5e-05 seconds\n", " bestEstimateColorScatterPlot_scl took 8e-05 seconds\n", " bestEstimateColorHistogram_scl took 7.5e-05 seconds\n", " dataScalingColorBoxPlot took 5.5e-05 seconds\n", "*** Merging into template file ***\n", " Rendering topSwitchboard took 0.000106 seconds\n", " Rendering maxLSwitchboard1 took 7.7e-05 seconds\n", " Rendering targetSpamBriefTable took 0.016061 seconds\n", " Rendering targetGatesBoxTable took 0.022216 seconds\n", " Rendering datasetOverviewTable took 0.00095 seconds\n", " Rendering bestGatesetSpamParametersTable took 0.002259 seconds\n", " Rendering bestGatesetSpamBriefTable took 0.02703 seconds\n", " Rendering bestGatesetSpamVsTargetTable took 0.002584 seconds\n", " Rendering bestGatesetGaugeOptParamsTable took 0.001192 seconds\n", " Rendering bestGatesetGatesBoxTable took 0.041716 seconds\n", " Rendering bestGatesetChoiEvalTable took 0.028531 seconds\n", " Rendering bestGatesetDecompTable took 0.019328 seconds\n", " Rendering bestGatesetEvalTable took 0.040963 seconds\n", " Rendering bestGermsEvalTable took 0.042405 seconds\n", " Rendering bestGatesetVsTargetTable took 0.001032 seconds\n", " Rendering bestGatesVsTargetTable_gv took 0.005566 seconds\n", " Rendering bestGatesVsTargetTable_gvgerms took 0.004299 seconds\n", " Rendering bestGatesVsTargetTable_gi took 0.004005 seconds\n", " Rendering bestGatesVsTargetTable_gigerms took 0.001905 seconds\n", " Rendering bestGatesVsTargetTable_sum took 0.005632 seconds\n", " Rendering bestGatesetErrGenBoxTable took 0.058627 seconds\n", " Rendering metadataTable took 0.003375 seconds\n", " Rendering stdoutBlock took 0.001003 seconds\n", " Rendering profilerTable took 0.001732 seconds\n", " Rendering softwareEnvTable took 0.002547 seconds\n", " Rendering exampleTable took 0.002842 seconds\n", " Rendering metricSwitchboard_gv took 3.9e-05 seconds\n", " Rendering metricSwitchboard_gi took 3.7e-05 seconds\n", " Rendering singleMetricTable_gv took 0.009076 seconds\n", " Rendering singleMetricTable_gi took 0.005141 seconds\n", " Rendering fiducialListTable took 0.00469 seconds\n", " Rendering prepStrListTable took 0.003329 seconds\n", " Rendering effectStrListTable took 0.002173 seconds\n", " Rendering colorBoxPlotKeyPlot took 0.00288 seconds\n", " Rendering germList2ColTable took 0.001626 seconds\n", " Rendering progressTable took 0.002298 seconds\n", " Rendering gramBarPlot took 0.002543 seconds\n", " Rendering progressBarPlot took 0.001903 seconds\n", " Rendering progressBarPlot_sum took 0.001655 seconds\n", " Rendering finalFitComparePlot took 0.001501 seconds\n", " Rendering bestEstimateColorBoxPlot took 0.026995 seconds\n", " Rendering bestEstimateTVDColorBoxPlot took 0.018741 seconds\n", " Rendering bestEstimateColorScatterPlot took 0.022944 seconds\n", " Rendering bestEstimateColorHistogram took 0.014919 seconds\n", " Rendering progressTable_scl took 0.000914 seconds\n", " Rendering progressBarPlot_scl took 0.000893 seconds\n", " Rendering bestEstimateColorBoxPlot_scl took 0.00091 seconds\n", " Rendering bestEstimateColorScatterPlot_scl took 0.000941 seconds\n", " Rendering bestEstimateColorHistogram_scl took 0.000899 seconds\n", " Rendering dataScalingColorBoxPlot took 0.000744 seconds\n", "Output written to example_files/easy_2q_report directory\n", "*** Report Generation Complete! Total time 50.8309s ***\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pygsti.report.create_standard_report(results, filename=\"example_files/easy_2q_report\",\n", " title=\"Example 2Q-GST Report\", verbosity=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now open [example_files/easy_2q_report/main.html](example_files/easy_2q_report/main.html) to see the results. You've run 2-qubit GST!\n", "\n", "You can save the `Results` object for later by just pickling it:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "with open(\"example_files/easy_2q_results.pkl\",\"wb\") as pklfile:\n", " pickle.dump(results, pklfile)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 1 }