{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating Readout Noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Theoretical Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Qubit-Readout can be corrupted in a variety of ways. The two most relevant error mechanisms on the Rigetti QPU right now are:\n", "\n", "1. Transmission line noise that makes a 0-state look like a 1-state or vice versa. We call this **classical readout bit-flip error**. This type of readout noise can be reduced by tailoring optimal readout pulses and using superconducting, quantum limited amplifiers to amplify the readout signal before it is corrupted by classical noise at the higher temperature stages of our cryostats.\n", "2. T1 qubit decay during readout (our readout operations can take more than a µsecond unless they have been specially optimized), which leads to readout signals that initially behave like 1-states but then collapse to something resembling a 0-state. We will call this **T1-readout error**. This type of readout error can be reduced by achieving shorter readout pulses relative to the T1 time, i.e., one can try to reduce the readout pulse length, or increase the T1 time or both." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Qubit measurements\n", "\n", "This section provides the necessary theoretical foundation for accurately modeling noisy quantum measurements on superconducting quantum processors. It relies on some of the abstractions (density matrices, Kraus maps) introduced in our notebook on [gate noise models](GateNoiseModels.ipynb).\n", "\n", "The most general type of measurement performed on a single qubit at a single time can be characterized by some set $\\mathcal{O}$ of measurement outcomes, e.g., in the simplest case $\\mathcal{O} = \\{0, 1\\}$, and some unnormalized quantum channels (see notebook on gate noise models) that encapsulate\n", "1. the probability of that outcome\n", "2. how the qubit state is affected conditional on the measurement outcome.\n", "\n", "Here the _outcome_ is understood as classical information that has been extracted from the quantum system.\n", "\n", "### Projective, ideal measurement\n", "The simplest case that is usually taught in introductory quantum mechanics and quantum information courses are Born's rule and the projection postulate which state that there exist a complete set of orthogonal projection operators \n", "$$\n", "P_{\\mathcal{O}} := \\{\\Pi_x \\text{ Projector }\\mid x \\in \\mathcal{O}\\},\n", "$$\n", "i.e., one for each measurement outcome. Any projection operator must satisfy $\\Pi_x^\\dagger = \\Pi_x = \\Pi_x^2$ and for an _orthogonal_ set of projectors any two members satisfy \n", "$$\n", "\\Pi_x\\Pi_y = \\delta_{xy} \\Pi_x = \\begin{cases} 0 & \\text{ if } x \\ne y \\\\ \\Pi_x & \\text{ if } x = y \\end{cases}\n", "$$\n", "and for a _complete_ set we additionally demand that $\\sum_{x\\in\\mathcal{O}} \\Pi_x = 1$.\n", "Following our introduction to gate noise, we write quantum states as density matrices as this is more general and in closer correspondence with classical probability theory.\n", "\n", "With these the probability of outcome $x$ is given by $p(x) = \\tr{\\Pi_x \\rho \\Pi_x} = \\tr{\\Pi_x^2 \\rho} = \\tr{\\Pi_x \\rho}$ and the post measurement state is\n", "$$\n", "\\rho_x = \\frac{1}{p(x)} \\Pi_x \\rho \\Pi_x,\n", "$$\n", "which is the projection postulate applied to mixed states.\n", "\n", "If we were a sloppy quantum programmer and accidentally erased the measurement outcome then our best guess for the post measurement state would be given by something that looks an awful lot like a Kraus map:\n", "$$\n", "\\rho_{\\text{post measurement}} = \\sum_{x\\in\\mathcal{O}} p(x) \\rho_x = \\sum_{x\\in\\mathcal{O}} \\Pi_x \\rho \\Pi_x.\n", "$$\n", "The completeness of the projector set ensures that the trace of the post measurement is still 1 and the Kraus map form of this expression ensures that $\\rho_{\\text{post measurement}}$ is a positive (semi-)definite operator.\n", "\n", "### Classical readout bit-flip error\n", "\n", "Consider now the ideal measurement as above, but where the outcome $x$ is transmitted across a noisy classical channel that produces a final outcome $x'\\in \\mathcal{O}' = \\{0', 1'\\}$ according to some conditional probabilities $p(x'|x)$ that can be recorded in the _assignment probability matrix_\n", "$$\n", "P_{x'|x} = \\begin{pmatrix} \n", "p(0 | 0) & p(0 | 1) \\\\\n", "p(1 | 0) & p(1 | 1)\n", "\\end{pmatrix}\n", "$$\n", "Note that this matrix has only two independent parameters as each column must be a valid probability distribution, i.e. all elements are non-negative and each column sums to 1.\n", "\n", "This matrix allows us to obtain the probabilities $\\mathbf{p}' := (p(x'=0), p(x'=1))^T$ from the original outcome probabilities $\\mathbf{p} := (p(x=0), p(x=1))^T$ via $\\mathbf{p}' = P_{x'|x}\\mathbf{p}$.\n", "The difference relative to the ideal case above is that now an outcome $x' = 0$ does not necessarily imply that the post measurement state is truly $\\Pi_{0} \\rho \\Pi_{0} / p(x=0)$. Instead, the post measurement state given a noisy outcome $x'$ must be\n", "\\begin{align}\n", "\\rho_{x'} & = \\sum_{x\\in \\mathcal{O}} p(x|x') \\rho_x \\\\\n", " & = \\sum_{x\\in \\mathcal{O}} p(x'|x)\\frac{p(x)}{p(x')} \\rho_x \\\\\n", " & = \\frac{1}{p(x')}\\sum_{x\\in \\mathcal{O}} p(x'|x) \\Pi_x \\rho \\Pi_x\n", "\\end{align}\n", "where \n", "\\begin{align}\n", "p(x') & = \\sum_{x\\in\\mathcal{O}} p(x'|x) p(x) \\\\\n", "& = \\tr{\\sum_{x\\in \\mathcal{O}} p(x'|x) \\Pi_x \\rho \\Pi_x} \\\\\n", "& = \\tr{\\rho \\sum_{x\\in \\mathcal{O}} p(x'|x)\\Pi_x} \\\\\n", "& = \\tr{\\rho E_x'}.\n", "\\end{align}\n", "where we have exploited the cyclical property of the trace $\\tr{ABC}=\\tr{BCA}$ and the projection property $\\Pi_x^2 = \\Pi_x$. This has allowed us to derive the noisy outcome probabilities from a set of positive operators\n", "$$\n", "E_{x'} := \\sum_{x\\in \\mathcal{O}} p(x'|x)\\Pi_x \\ge 0\n", "$$\n", "that must sum to 1: \n", "$$\n", "\\sum_{x'\\in\\mathcal{O}'} E_x' = \\sum_{x\\in\\mathcal{O}}\\underbrace{\\left[\\sum_{x'\\in\\mathcal{O}'} p(x'|x)\\right]}_{1}\\Pi_x = \\sum_{x\\in\\mathcal{O}}\\Pi_x = 1.\n", "$$\n", "The above result is a type of generalized **Bayes' theorem** that is extremely useful for this type of (slightly) generalized measurement and the family of operators $\\{E_{x'}| x' \\in \\mathcal{O}'\\}$ whose expectations give the probabilities is called a **positive operator valued measure** (POVM). These operators are not generally orthogonal nor valid projection operators but they naturally arise in this scenario. This is not yet the most general type of measurement, but it will get us pretty far." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to model $T_1$ error\n", "T1 type errors fall outside our framework so far as they involve a scenario in which the _quantum state itself_ is corrupted during the measurement process in a way that potentially erases the pre-measurement information as opposed to a loss of purely classical information. The most appropriate framework for describing this is given by that of measurement instruments, but for the practical purpose of arriving at a relatively simple description, we propose describing this by a T1 damping Kraus map followed by the noisy readout process as described above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Further reading\n", "Chapter 3 of John Preskill's lecture notes http://www.theory.caltech.edu/people/preskill/ph229/notes/chap3.pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How do I get started?\n", "\n", "1. Come up with a good guess for your readout noise parameters $p(0|0)$ and $p(1|1)$, the off-diagonals then follow from the normalization of $P_{x'|x}$. If your assignment fidelity $F$ is given, and you assume that the classical bit flip noise is roughly symmetric, then a good approximation is to set $p(0|0)=p(1|1)=F$.\n", "2. For your QUIL program `p`, and a qubit index `q` call:\n", " ```\n", " p.define_noisy_readout(q, p00, p11)\n", " ```\n", " where you should replace `p00` and `p11` with the assumed probabilities.\n", "\n", "### Estimate $P_{x'|x}$ yourself!\n", "You can also run some simple experiments to estimate the assignment probability matrix directly from a QPU.\n", "\n", "**Scroll down for some examples!**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from pyquil.quil import Program, MEASURE, Pragma\n", "from pyquil.api import QVMConnection\n", "from pyquil.gates import I, X, RX, H, CNOT\n", "from pyquil.noise import (estimate_bitstring_probs, correct_bitstring_probs, \n", " bitstring_probs_to_z_moments, estimate_assignment_probs)\n", "\n", "DARK_TEAL = '#48737F'\n", "FUSCHIA = '#D6619E'\n", "BEIGE = '#EAE8C6'\n", "\n", "cxn = QVMConnection()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Rabi sequence with noisy readout" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 875 ms, sys: 50.4 ms, total: 925 ms\n", "Wall time: 2.23 s\n" ] } ], "source": [ "%%time\n", "\n", "# number of angles\n", "num_theta = 101\n", "\n", "# number of program executions\n", "trials = 200\n", "\n", "thetas = np.linspace(0, 2*np.pi, num_theta)\n", "\n", "p00s = [1., 0.95, 0.9, 0.8]\n", "\n", "results_rabi = np.zeros((num_theta, len(p00s)))\n", "\n", "for jj, theta in enumerate(thetas):\n", " for kk, p00 in enumerate(p00s):\n", " cxn.random_seed = hash((jj, kk))\n", " p = Program(RX(theta, 0))\n", " ro = p.declare(\"ro\")\n", " # assume symmetric noise p11 = p00\n", " p.define_noisy_readout(0, p00=p00, p11=p00)\n", " p.measure(0, ro[0])\n", " res = cxn.run(p, [0], trials=trials)\n", " results_rabi[jj, kk] = np.sum(res)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Effect of classical readout noise on Rabi contrast.')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "for jj, (p00, c) in enumerate(zip(p00s, [DARK_TEAL, FUSCHIA, \"k\", \"gray\"])):\n", " plt.plot(thetas, results_rabi[:, jj]/trials, c=c, label=r\"$p(0|0)=p(1|1)={:g}$\".format(p00))\n", "plt.legend(loc=\"best\")\n", "plt.xlim(*thetas[[0,-1]])\n", "plt.ylim(-.1, 1.1)\n", "plt.grid(alpha=.5)\n", "plt.xlabel(r\"RX angle $\\theta$ [radian]\", size=16)\n", "plt.ylabel(r\"Excited state fraction $n_1/n_{\\rm trials}$\", size=16)\n", "plt.title(\"Effect of classical readout noise on Rabi contrast.\", size=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: Estimate the assignment probabilities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate assignment probabilities for a perfect quantum computer" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/peter/.pyenv/versions/3.6.3/envs/forest-tutorials/lib/python3.6/site-packages/pyquil/quil.py:979: UserWarning: Please DECLARE all memory. I'm adding a declaration for the `ro` register, but I won't do this for you in the future.\n", " \"Please DECLARE all memory. I'm adding a declaration for the `ro` register, \"\n" ] }, { "data": { "text/plain": [ "array([[1., 0.],\n", " [0., 1.]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimate_assignment_probs(0, 1000, cxn, Program())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Re-Estimate assignment probabilities for an imperfect quantum computer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cxn.seed = None\n", "header0 = Program().define_noisy_readout(0, .85, .95)\n", "header1 = Program().define_noisy_readout(1, .8, .9)\n", "header2 = Program().define_noisy_readout(2, .9, .85)\n", "\n", "ap0 = estimate_assignment_probs(0, 100000, cxn, header0)\n", "ap1 = estimate_assignment_probs(1, 100000, cxn, header1)\n", "ap2 = estimate_assignment_probs(2, 100000, cxn, header2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.84949 0.05007]\n", " [0.15051 0.94993]]\n", "[[0.79957 0.10064]\n", " [0.20043 0.89936]]\n", "[[0.89964 0.15048]\n", " [0.10036 0.84952]]\n" ] } ], "source": [ "print(ap0, ap1, ap2, sep=\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3: Use `pyquil.noise.correct_bitstring_probs` to correct for noisy readout\n", "\n", "### 3a) Correcting the Rabi signal from above" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "ap_last = np.array([[p00s[-1], 1 - p00s[-1]], \n", " [1 - p00s[-1], p00s[-1]]])\n", "corrected_last_result = [correct_bitstring_probs([1-p, p], [ap_last])[1] for p in results_rabi[:, -1] / trials]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Corrected contrast')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(14, 6))\n", "for jj, (p00, c) in enumerate(zip(p00s, [DARK_TEAL, FUSCHIA, \"k\", \"gray\"])):\n", " if jj not in [0, 3]:\n", " continue\n", " plt.plot(thetas, results_rabi[:, jj]/trials, c=c, label=r\"$p(0|0)=p(1|1)={:g}$\".format(p00), alpha=.3)\n", "plt.plot(thetas, corrected_last_result, c=\"red\", label=r\"Corrected $p(0|0)=p(1|1)={:g}$\".format(p00s[-1]))\n", "plt.legend(loc=\"best\")\n", "plt.xlim(*thetas[[0,-1]])\n", "plt.ylim(-.1, 1.1)\n", "plt.grid(alpha=.5)\n", "plt.xlabel(r\"RX angle $\\theta$ [radian]\", size=16)\n", "plt.ylabel(r\"Excited state fraction $n_1/n_{\\rm trials}$\", size=16)\n", "plt.title(\"Corrected contrast\", size=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**We find that the corrected signal is fairly noisy (and sometimes exceeds the allowed interval $[0,1]$) due to the overall very small number of samples $n=200$.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3b) In this example we will create a GHZ state $\\frac{1}{\\sqrt{2}}\\left[\\left|000\\right\\rangle + \\left|111\\right\\rangle \\right]$ and measure its outcome probabilities with and without the above noise model. We will then see how the Pauli-Z moments that indicate the qubit correlations are corrupted (and corrected) using our API." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "DECLARE ro BIT[3]\n", "H 0\n", "CNOT 0 1\n", "CNOT 1 2\n", "MEASURE 0 ro[0]\n", "MEASURE 1 ro[1]\n", "MEASURE 2 ro[2]\n", "\n" ] } ], "source": [ "ghz_prog = Program()\n", "ro = ghz_prog.declare(\"ro\", \"BIT\", 3)\n", "ghz_prog += H(0)\n", "ghz_prog += CNOT(0, 1)\n", "ghz_prog += CNOT(1, 2)\n", "ghz_prog += MEASURE(0, ro[0])\n", "ghz_prog += MEASURE(1, ro[1])\n", "ghz_prog += MEASURE(2, ro[2])\n", "print(ghz_prog)\n", "results = cxn.run(ghz_prog, [0, 1, 2], trials=10000)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PRAGMA READOUT-POVM 0 \"(0.85 0.050000000000000044 0.15000000000000002 0.95)\"\n", "PRAGMA READOUT-POVM 1 \"(0.8 0.09999999999999998 0.19999999999999996 0.9)\"\n", "PRAGMA READOUT-POVM 2 \"(0.9 0.15000000000000002 0.09999999999999998 0.85)\"\n", "DECLARE ro BIT[3]\n", "H 0\n", "CNOT 0 1\n", "CNOT 1 2\n", "MEASURE 0 ro[0]\n", "MEASURE 1 ro[1]\n", "MEASURE 2 ro[2]\n", "\n" ] } ], "source": [ "header = header0 + header1 + header2\n", "noisy_ghz = header + ghz_prog\n", "print(noisy_ghz)\n", "noisy_results = cxn.run(noisy_ghz, [0, 1, 2], trials=10000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Uncorrupted probability for $\\left|000\\right\\rangle$ and $\\left|111\\right\\rangle$" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4959, 0.5041)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "probs = estimate_bitstring_probs(results)\n", "probs[0, 0, 0], probs[1, 1, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected the outcomes `000` and `111` each have roughly probability $1/2$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Corrupted probability for $\\left|000\\right\\rangle$ and $\\left|111\\right\\rangle$" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.3093, 0.3611)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noisy_probs = estimate_bitstring_probs(noisy_results)\n", "noisy_probs[0, 0, 0], noisy_probs[1, 1, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The noise-corrupted outcome probabilities deviate significantly from their ideal values!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Corrected probability for $\\left|000\\right\\rangle$ and $\\left|111\\right\\rangle$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5054052808756202, 0.4957404969469219)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrected_probs = correct_bitstring_probs(noisy_probs, [ap0, ap1, ap2])\n", "corrected_probs[0, 0, 0], corrected_probs[1, 1, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The corrected outcome probabilities are much closer to the ideal value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=100, 010, 001$ from non-noisy data\n", "*We expect these to all be very small*" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(-0.008199999999999985, -0.008199999999999985, -0.008199999999999985)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmoments = bitstring_probs_to_z_moments(probs)\n", "zmoments[1, 0, 0], zmoments[0, 1, 0], zmoments[0, 0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=110, 011, 101$ from non-noisy data\n", "*We expect these to all be close to 1.*" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.0, 1.0, 1.0)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmoments[1, 1, 0], zmoments[0, 1, 1], zmoments[1, 0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=100, 010, 001$ from noise-corrected data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.005804208050837423, 0.012290215042994768, 0.009450584654813854)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmoments_corr = bitstring_probs_to_z_moments(corrected_probs)\n", "zmoments_corr[1, 0, 0], zmoments_corr[0, 1, 0], zmoments_corr[0, 0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Estimate $\\langle Z_0^{j} Z_1^{k} Z_2^{\\ell}\\rangle$ for $jkl=110, 011, 101$ from noise-corrected data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.0242766053748604, 0.9909609480429594, 0.9893455578723482)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zmoments_corr[1, 1, 0], zmoments_corr[0, 1, 1], zmoments_corr[1, 0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Overall the correction can restore the contrast in our multi-qubit observables, though we also see that the correction can lead to slightly non-physical expectations. This effect is reduced the more samples we take." ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }