{ "cells": [ { "cell_type": "markdown", "id": "fcefa3cf", "metadata": {}, "source": [ "# Snake Charge Scan\n", "\n", "To manipulate an electron confined in a gate-defined quantum dot, it is essential to control the number of electrons i.e. the chemical potential of each quantum dot and the tunnel coupling among quantum dots. The charge stability diagram (CSD) shows changes in the electron occupation of the system in response to changes in applied gate voltages. This information can be used to extract the operation point for further experiments. For an infinitely slow gate sweep and in absence of charging effects the CSD shows the charge occupation of the ground state. In the real world however, the CSD can depend on the sweep direction of gate voltages and a charge state hysteresis in quantum dots has been observed and investigated. [1]\n", "\n", "[1] C. H. Yang, et al., Appl. Phys. Lett. 105, 183505 (2014)\n", "\n", "In this tutorial, a pulse for the bi-directional sweep of a CSD is constructed so that the hysteresis of charge occupancy in a double quantum dot system can be measured. For ease of analysis, 2 different measurement windows namingly `('x_neg', 'x_pos')` are defined providing the possibliy of inspecting two sweep direction individually. Options of `plot` function in `qupulse.pulses.plotting` will be explored as well." ] }, { "cell_type": "markdown", "id": "394631b6", "metadata": {}, "source": [ "## Create a linear voltage sweep\n", "\n", "The basic building block of any such a diagram is a linear sweep of a channel. The first thing we need to do is write such a sweep. There are two options to implement this on a conceptional level.\n", "\n", "The first is to interpret the sweep as a continuous change of the voltage in time. The number of points here is just determined by the time resolution of our readout. Each readout point represents an average over a short time window of this continuous sweep.\n", "\n", "The second approach is to change the applied voltage in a fixed number of steps. Here the number of points is already a property of the pulse and each readout point is the average over the output at that point.\n", "\n", "In the following we will explore both variants and their hardware feasibility.\n", "\n", "### Continuous voltage sweep\n", "\n", "Let us first explore the first option. We can use a `TablePT` or a `PointPT` to implement it." ] }, { "cell_type": "code", "execution_count": 1, "id": "7b6be5b4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "from qupulse.pulses import *\n", "from qupulse.plotting import *\n", "\n", "linear_cont = TablePT({'X': [(0, 'x_start'),\n", " ('tx_sweep', 'x_stop', 'linear')]},\n", " measurements=[('M', 0, 'tx_sweep')])\n", "\n", "x_sweep_params = {'x_start': -3.3, 'x_stop': -1.5, 'tx_sweep': 10.}\n", "\n", "_ = plot(linear_cont, parameters=x_sweep_params, stepped=False, plot_measurements={'M'})" ] }, { "cell_type": "markdown", "id": "dd2a62bd", "metadata": {}, "source": [ "You'll notice that this pulse does only have one measurement window over the whole range attached. For a sweep with `n` points we would maybe expect `n` measurement windows. However, measurement windows require a pulse template to be attached to and there is no way in qupulse (yet in 2023) to change or parameterize the number of measurement windows directly. You have to change the number of pulse template instantiations f.i. with a `RepetitionPT` or a `ForLoopPT` to change the number of measurement windows.\n", "\n", "We can however interpret the measurement window as the time window where the sweep happens and use it as such in our measurement configuration and data analysis. qupulse does not enforce or promote a particular meaning of measurement windows. qupulse simply tracks them through complex pulse trees and gives you their final position in an instantiated pulse.\n", "\n", "### Stepped sweep\n", "\n", "Next we will explore how to write a \"stepped\" sweep. We want the number of steps to be parameterized by `n_x` and the range by `x_start` and `x_stop`. The total duration of the sweep shall be `tx_sweep`." ] }, { "cell_type": "code", "execution_count": 2, "id": "fcdfe86c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "step = ConstantPT(duration='tx_sweep / n_x', amplitude_dict={'X': 'x_start + x_i * (x_stop - x_start) / (n_x - 1)'},\n", " measurements=[('M', 0, 'tx_sweep / n_x')])\n", "\n", "# equivalent to ForLoopPT(step, 'x_i', 'n_x')\n", "linear_step = step.with_iteration('x_i', 'n_x')\n", "\n", "x_sweep_params.update(n_x=10)\n", "\n", "_ = plot(linear_step, parameters=x_sweep_params, plot_measurements={'M'})" ] }, { "cell_type": "markdown", "id": "205f94ed", "metadata": {}, "source": [ "Here we see that each step has its own measurement window.\n", "\n", "## The snake: going forward and backward\n", "\n", "We can now utilize the `TimeReversalPT` to go backward and combine both direction with a `SequencePT`. Furthermore, we want to rename the measurement windows to discriminate between forward and backward measurements. We can utilize the `with_*` methods and the overloaded matrix multiplication operator `@` to make this very concise." ] }, { "cell_type": "code", "execution_count": 3, "id": "8ac1f53d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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DRRxXsyY1a9akRYsWKW9HCUVEKqRqVarS/LgTAXhlygyu6zcg5W29MmVspe6/aN1KOvTsmHL/YrrkJSIikdAZihwT0qkLXx5qwueaatJLFHSGIseEdOrC57omfK6pJr1ERWcocsxIpy58LmvC55pq0ktUdIYiIiKRUEIREZFIKKGIiEgklFBERCQSSigiIhKJXJUAftzMlprZQjMba2b1ErTrbWbLzGylmQ2LWd7AzKaY2Yrwe/2sBS8iInHl6gxlCnCOu58HLAfuK9nAzKoCvwOuBNoDg82sfbh6GDDN3dsA08J5ERHJoZwkFHef7O5F4ewcIN5oZBcCK919tbvvA14E+ofr+gOjwulRwDUZDFdERJJQHu6h3Aa8EWf5ycAnMfPrw2UATd19I0D4vUmijZvZUDObZ2bzCgoKIgpZRERKytib8mY2FWgWZ9Vwd389bDMcKAJGx9tEnGVHXfjA3UcCIwHy8vJUOEFEJEMyllDc/fLS1pvZEOBqoKfHr5CzHjglZr4FsCGc3mRmzd19o5k1BzZHEbOIiKQuV0959QbuBfq5+54Ezd4D2phZKzOrAQwCxoXrxgFDwukhwOuZjFdERMqWq3soTwN1gClmtsDMfg9gZieZ2USA8Kb9HcCbwBLgZXcvHsHvEaCXma0AeoXzIiKSQzkZbdjdz0iwfAPQJ2Z+IjAxTrutQM+MBSgiIketPDzlJSIixwAlFBERiYQSioiIRKLMeyhmlgdcDJwEFAKLganuvi3DsUklkk5NeFBd+FxTTXqBUs5QzOxWM3ufYJytWsAygvc9uhM8nTXKzE7NTphyrEunJjyoLnwuqSa9FCvtDKU2kO/uhfFWmlkHoA2wLgNxSSWUTk14qNx14XNJNemlWMKE4u6/K62juy+IPBoREamwUropb2ZXRx2IiIhUbKk+5XVBpFGIiEiFl1JCcfcHow5EREQqtmQeG/5mvOXu/pfowxERkYoqmbG8Yi9v1SQYQ+t9QAlFREQOKTOhuPt/xc6bWV3grxmLSEREKqRU7qHsIXj/RERE5JBk7qH8g69K71YB2gMvZzIoERGpeJK5h/JEzHQRsNbd12coHhERqaCSuYfydtQ7NbPHgb7APmAV8C13/6JEm1MIbvw3Aw4CI9391+G6EcB3gIKw+f1hMS4REcmRVN+UH5nmfqcA57j7ecByggEoSyoC7nb3dkAX4Ptm1j5m/VPu3iH8UjIREcmxVN+U/0M6O3X3yWHNeIA5QIs4bTa6+/vh9E6CuvInp7NfERHJnFTflJ8fYQy3AW+U1sDMWgIdgbkxi+8ws4Vm9pyZ1S+l71Azm2dm8woKChI1ExGRNJWZUMyssZk9YWYTzeyt4q8k+k01s8VxvvrHtBlOcGlrdCnbOQF4FbjT3XeEi58BWgMdgI3Ak4n6u/tId89z97zGjRuXFbaIiKQomae8RgMvAVcBtwND+OpmeELufnlp681sCHA10NPdPUGb6gTJZLS7j4nZ9qaYNs8C48v+MUREJJOSueTV0N3/BOx397fd/TaCm+QpM7PewL1AP3ffk6CNAX8Clrj7L0usax4zO4CgLLGIiORQMmco+8PvG83sKmADcW6iH6WngeMISgkDzHH3283sJOCP7t4HyAduARaZ2YKwX/HjwY+FFSMdWAN8N814JE2qCS/pUE36Y0MyCeXhcPyuu4HfAicCd6WzU3c/I8HyDUCfcHomYAna3ZLO/iV6xTXhG6TYv1W9WtTfsTnSmKRi6NO6Ppu3bkm5/9JthWx21aQvD5J5sbH4/sR2oEdmw5GKTDXhJRWqSX/sSHgPxcx+ZGYJ/+A0s8tUClhERIqVdoayCPiHme0lqH9SQFAPpQ3B47pTgZ9nOkAREakYEiYUd38deN3M2hDcIG8O7AD+Bgx198LshCgiIhVBMvdQVgCpP34hIiKVQqpjeYmIiBxGCUVERCKhhCIiIpFIZnDIM81smpktDufPM7MfZT40ERGpSJI5Q3mWoADWfgB3XwgMymRQIiJS8SSTUI5393+VWFYUt6WIiFRaySSULWbWmmAgRszsWoIaJCIiIockMzjk94GRwFlm9inwMXBzRqMSEZEKJ5kXG1cDl5tZbaBKWN9dRETkMGUmFDP77xLzEIw8PN/dF2QmLBERqWiSuYeSR1D69+TwayhwKfCsmd2TudBERKQiSaoEMNDJ3e9297sJEkxj4BLg1lR2amaPm9lSM1toZmPNrF6CdmvMbJGZLTCzeTHLG5jZFDNbEX6vn0ocIiISnWQSyqnAvpj5/cBp4WjDX6a43ynAOe5+HrCc4D2XRHq4ewd3z4tZNgyY5u5tgGnhvIiI5FAyT3k9D8wxs9fD+b7AC+FN+o9S2am7T46ZnQNce5Sb6E9w2Q1gFDADuDeVWCSgmvBSkakmfflQ5hmKuz9EcN/kC4Kb8be7+0/dfbe73xRBDLcBbyTaPTDZzOab2dCY5U3dfWMY30agSaKNm9lQM5tnZvMKCgoiCPfYVFwTPlWt6tWidTWVYZXs69O6Ps2q7Su7YQJLtxWy6EvVpI9CMmcouPs8M1tHULERMzvV3deV1sfMpgLN4qwaHhbvwsyGE7x1PzrBZvLdfYOZNQGmmNlSd38nmZhjYh9J8B4NeXl5fjR9KxvVhJeKSDXpy49kHhvuBzwJnARsJrinshQ4u7R+7n55GdsdAlwN9HT3uL/o3X1D+H2zmY0FLgTeATaZWXN332hmzcO4REQkh5K5Kf8Q0AVY7u6tgMuBWens1Mx6E9zz6OfuexK0qW1mdYqngSuAxeHqccCQcHoI8PqRWxARkWxKJqHsd/etQBUzq+Lu04EOae73aaAOwWWsBWb2ewAzO8nMJoZtmgIzzewD4F/ABHefFK57BOhlZiuAXuG8iIjkUDL3UL4wsxMILjWNNrPNpDnasLufkWD5BqBPOL0aOD9Bu61Az3RiEBGRaCVzhtIf2APcBUwCVhHc+xARETkkmYTygLsfdPcidx/l7r9B73yIiEgJySSUXnGWXRl1ICIiUrElvIdiZv8JfA843cwWxqyqQ5pPeYmIyLGntJvyzxO8wf4LDh8ra6e7b8toVCIiUuGUllCqAjsIKjYexswaKKmIiEis0hLKfMI68oCVWOfA6RmJSEREKqSECSV8K15ERCQpSQ0OGY7ndUk4O8Pdx2cuJBERqYjKfGzYzB4BfkBQ++Qj4Adm9otMByYiIhVLMmcofYAO7n4QwMxGAf+m9CqLIiJSySTzYiNAvZhpFeYTEZEjJHOG8gvg32Y2neBpr0vQ2YmIiJRQ2pvyTwPPu/sLZjYDuIAgodzr7p9lKT5JkmrCi6RONemjUdoZygrgybAi4kvAC+6+ICtRyVErrgnfIMX+rerVov4OFb6UyqdP6/ps3rol5f5LtxWy2VWTHkp/D+XXwK/N7DRgEPBnM6sJvAC86O7LsxSjJEk14UWOnmrSR6fMm/LuvtbdH3X3jsCNwABgSTo7NbPHzWypmS00s7FmVi9Om7ZhNcfirx1mdme4boSZfRqzrk868YiISPqSeQ+lupn1NbPRBINFLge+keZ+pwDnuPt54faOuMnv7svcvYO7dwA6ExT5GhvT5Kni9e4+sWR/ERHJrtJuyvcCBgNXEdR0fxEY6u5pn9u5++SY2TnAtWV06Qmscve16e5bREQyo7QzlPuBfwLt3L2vu4+OIpnEcRvBmU9pBhHcu4l1R3jJ7Dkzq5+oo5kNNbN5ZjavoKAg3VhFRCSBhAnF3Xu4+7OpDlNvZlPNbHGcr/4xbYYDRcDoUrZTA+gHvBKz+BmgNdAB2Ag8WcrPMdLd89w9r3Hjxqn8KCIikoSkBodMhbtfXtp6MxsCXA30dHcvpemVwPvuvilm24emzexZQINViojkWLJDr0TKzHoD9wL93H1PGc0HU+JyV/huTLEBwOJoIxQRkaOVk4QCPE1Qm35K+Njv7wHM7CQzO/TElpkdD/QCxpTo/5iZLQpr3fcA7spS3CIikkDGLnmVxt3PSLB8A8HoxsXze4CGcdrdkrnoREQkFbk6QxERkWOMEoqIiERCCUVERCKhhCIiIpFQQhERkUgooYiISCSUUEREJBJKKCIiEomcvNgoR1JNeJGKSzXpAzpDKSeKa8KnqlW9WrSupjKkItnWp3V9mlXbl3L/pdsKWfTlsVGTXmco5YhqwotUPKpJ/xWdoYiISCSUUEREJBJKKCIiEgklFBERiYQSioiIRCJXJYAfMrOFYbXGyWZ2UoJ2vc1smZmtNLNhMcsbmNkUM1sRfq+fvehFRCSeXJ2hPO7u57l7B2A88EDJBmZWFfgdcCXQHhhsZu3D1cOAae7eBpgWzouISA7lJKG4+46Y2dqAx2l2IbDS3Ve7+z7gRaB/uK4/MCqcHgVck6FQRUQkSTl7sdHMfgZ8E9gO9IjT5GTgk5j59cBF4XRTd98I4O4bzaxJKfsZCgwFOPXUUyOIXERE4snYGYqZTTWzxXG++gO4+3B3PwUYDdwRbxNxlsU7kymVu4909zx3z2vcuPHRdhcRkSRl7AzF3S9PsunzwATgwRLL1wOnxMy3ADaE05vMrHl4dtIc2JxWsCIikrZcPeXVJma2H7A0TrP3gDZm1srMagCDgHHhunHAkHB6CPB6pmIVEZHk5OoeyiNm1hY4CKwFbgcIHx/+o7v3cfciM7sDeBOoCjzn7sWjHz4CvGxm3wbWwTEx8rOISIWWk4Ti7t9IsHwD0CdmfiIwMU67rUDPjAUoIiJHTW/Ki4hIJJRQREQkEkooIiISCSUUERGJhEoAR2TyqgLG72nK7OnLUur/8ReF1I04JhGpGD4rqsGtE1ak3L9pYZ1y8airzlAi8s66bWw9UCPl/q3q1aJ1tWOjrrSIJK9P6/o0q7Yv5f5LtxWy6MvaEUaUOp2hRKhh1X083KNtyv0nvvFh2Y1E5Jhy/VmNsOXvct1VXVPqf+uEFWzeWj7+GNUZioiIREIJRUREIqGEIiIikVBCERGRSCihiIhIJJRQREQkEkooIiISCSUUERGJhBKKiIhEIlclgB8ys4VmtsDMJoeVGku2OcXMppvZEjP70Mx+ELNuhJl9GvZfYGZ9SvYXEZHsytUZyuPufp67dwDGAw/EaVME3O3u7YAuwPfNrH3M+qfcvUP4dURVRxERya6cJBR33xEzWxvwOG02uvv74fROYAlwcnYiFBGRo5Wzeyhm9jMz+wS4ifhnKLFtWwIdgbkxi+8IL5s9Z2b1MxepiIgkI2MJxcymmtniOF/9Adx9uLufAowG7ihlOycArwJ3xpzZPAO0BjoAG4EnS+k/1Mzmmdm8goKCaH44ERE5QsaGr3f3y5Ns+jwwAXiw5Aozq06QTEa7+5iYbW+KafMswX2YRHGMBEYC5OXlHXFpTUREopGrp7zaxMz2A5bGaWPAn4Al7v7LEuuax8wOABZnIk4REUlergpsPWJmbYGDwFrgdoDw8eE/unsfIB+4BVhkZgvCfveHT3Q9ZmYdCG7mrwG+m9XoRUTkCDlJKO7+jQTLNwB9wumZgCVod0vUMf3kHx8ya/EeqtdQTXgRqVjSrUnfqMY+uvRMPw69KR8R1YQXkVxItyZ9lFRTPvRg37N5s/pWmjQ5M+VtqCa8iGRbujXpARatWxlJLDpDERGRSCihiIhIJJRQREQkEkooIiISCSUUERGJhBKKiIhEQglFREQioYQiIiKRUEIREZFIKKGIiEgklFBERCQSSigiIhIJJRQREYmEEoqIiERCCUVERCKhhCIiIpEwd891DFljZgUENewTaQRsyVI4qVB8qSvPsYHiS5fiS09Z8Z3m7o3L2kilSihlMbN57p6X6zgSUXypK8+xgeJLl+JLT1Tx6ZKXiIhEQglFREQioYRyuJG5DqAMii915Tk2UHzpUnzpiSQ+3UMREZFI6AxFREQioYQiIiKRqJQJxcx6m9kyM1tpZsPirDcz+024fqGZdcpibKeY2XQzW2JmH5rZD+K0udTMtpvZgvDrgSzGt8bMFoX7nRdnfS6PXduYY7LAzHaY2Z0l2mT12JnZc2a22cwWxyxrYGZTzGxF+L1+gr6lfk4zGN/jZrY0/Pcba2b1EvQt9bOQwfhGmNmnMf+GfRL0zdXxeykmtjVmtiBB34wev0S/SzL6+XP3SvUFVAVWAacDNYAPgPYl2vQB3gAM6ALMzWJ8zYFO4XQdYHmc+C4Fxufo+K0BGpWyPmfHLs6/82cEL2Tl7NgBlwCdgMUxyx4DhoXTw4BHE8Rf6uc0g/FdAVQLpx+NF18yn4UMxjcC+J8k/v1zcvxKrH8SeCAXxy/R75JMfv4q4xnKhcBKd1/t7vuAF4H+Jdr0B/7igTlAPTNrno3g3H2ju78fTu8ElgAnZ2PfEcnZsSuhJ7DK3UsbGSHj3P0dYFuJxf2BUeH0KOCaOF2T+ZxmJD53n+zuReHsHKBF1PtNVoLjl4ycHb9iZmbA9cALUe83GaX8LsnY568yJpSTgU9i5tdz5C/sZNpknJm1BDoCc+Os7mpmH5jZG2Z2dhbDcmCymc03s6Fx1peLYwcMIvF/5Fwdu2JN3X0jBP/pgSZx2pSX43gbwRlnPGV9FjLpjvCS3HMJLtmUh+N3MbDJ3VckWJ+141fid0nGPn+VMaFYnGUln51Opk1GmdkJwKvAne6+o8Tq9wku5ZwP/BZ4LYuh5bt7J+BK4PtmdkmJ9eXh2NUA+gGvxFmdy2N3NMrDcRwOFAGjEzQp67OQKc8ArYEOwEaCy0ol5fz4AYMp/ewkK8evjN8lCbvFWVbm8auMCWU9cErMfAtgQwptMsbMqhN8AEa7+5iS6919h7vvCqcnAtXNrFE2YnP3DeH3zcBYglPjWDk9dqErgffdfVPJFbk8djE2FV8GDL9vjtMm15/BIcDVwE0eXlQvKYnPQka4+yZ3P+DuB4FnE+w318evGjAQeClRm2wcvwS/SzL2+auMCeU9oI2ZtQr/kh0EjCvRZhzwzfCJpS7A9uJTxEwLr7v+CVji7r9M0KZZ2A4zu5Dg33FrFmKrbWZ1iqcJbt4uLtEsZ8cuRsK/DHN17EoYBwwJp4cAr8dpk8znNCPMrDdwL9DP3fckaJPMZyFT8cXekxuQYL85O36hy4Gl7r4+3spsHL9Sfpdk7vOXqScMyvMXwZNIywmeYhgeLrsduD2cNuB34fpFQF4WY+tOcGq5EFgQfvUpEd8dwIcET17MAbplKbbTw31+EO6/XB27cP/HEySIujHLcnbsCBLbRmA/wV993wYaAtOAFeH3BmHbk4CJpX1OsxTfSoLr58Wfv9+XjC/RZyFL8f01/GwtJPgl17w8Hb9w+f8Vf+Zi2mb1+JXyuyRjnz8NvSIiIpGojJe8REQkA5RQREQkEkooIiISCSUUERGJhBKKiIhEQglFJElm1jBmFNnPYka83WVm/5uhfd5pZt9MoV8NM3snfMFOJCv02LBICsxsBLDL3Z/I4D6qEQwV08m/GqzxaPo/SDDAX6KhU0QipTMUkTRZUGNlfDg9wsxGmdnksN7FQDN7LKx7MSkcCgMz62xmb4cDA76ZYETmywiGkCkK+8wws0fN7F9mttzMLg6Xnx0uWxAOmNgm7P8acFPGD4BISAlFJHqtgasIhvv+GzDd3c8FCoGrwqTyW+Bad+8MPAf8LM528oH5JZZVc/cLgTuBB8NltwO/dvcOQB7BG9sQDOVxQUQ/k0iZdH1VJHpvuPt+M1tEUKhoUrh8EdASaAucA0wJhxWrSjB8R0nNCWpYxCoe4G9+uC2AfwLDzawFMMbD4dLd/YCZ7TOzOh7UwxDJKCUUkeh9CeDuB81sv391o/Igwf85Az50965lbKcQqBlv28CBcFu4+/NmNpfgrOhNM/sPd38rbHccsDetn0YkSbrkJZJ9y4DGZtYVgiHGExT6WgKcUdbGzOx0YLW7/4ZgsMTzwuUNgQJ33x9Z5CKlUEIRyTIPSqpeCzxqZh8QjALbLU7TNwhqlpflBmCxmS0AzgL+Ei7vAUxMN16RZOmxYZFyzMzGAvd44jKypfUdA9zn7suij0zkSDpDESnfhhHcnD8qYVGk15RMJJt0hiIiIpHQGYqIiERCCUVERCKhhCIiIpFQQhERkUgooYiISCT+PwtzGuvzaupKAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "snake_1d_cont = linear_cont.with_mapping({'M': 'x_fwd'}) @ linear_cont.with_time_reversal().with_mapping({'M': 'x_bwd'})\n", "snake_1d_step = linear_step.with_mapping({'M': 'x_fwd'}) @ linear_step.with_time_reversal().with_mapping({'M': 'x_bwd'})\n", "\n", "_ = plot(snake_1d_cont, parameters=x_sweep_params, plot_measurements={'x_fwd', 'x_bwd'}, stepped=False)\n", "_ = plot(snake_1d_step, parameters=x_sweep_params, plot_measurements={'x_fwd', 'x_bwd'})" ] }, { "cell_type": "markdown", "id": "872a0324", "metadata": {}, "source": [ "## Two-dimensional sweep\n", "\n", "The next step is to make two-dimensional scans. We need to add the `Y` channel and loop over the desired voltages. We can use the `ParallelChannelPT` and `ForLoopPT` for that. The number of points in y-direction shall be `n_y`." ] }, { "cell_type": "code", "execution_count": 4, "id": "c66d64e0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "y_value = {'Y': 'y_start + y_i * (y_stop - y_start) / (n_y - 1)'}\n", "\n", "# with_* methods\n", "snake_step = snake_1d_step\\\n", " .with_parallel_channels(y_value)\\\n", " .with_iteration('y_i', 'n_y')\n", "\n", "# Direct class instantiation\n", "snake_linear = ForLoopPT(ParallelChannelPT(snake_1d_cont, y_value), 'y_i', 'n_y')\n", "\n", "sweep_params_2d = {**x_sweep_params, 'y_start': -1.1, 'y_stop': 0.6, 'n_y': 4}\n", "\n", "_, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", "_ = plot(snake_linear, parameters=sweep_params_2d, plot_measurements={'x_fwd', 'x_bwd'}, stepped=False, axes=ax1)\n", "_ = plot(snake_step, parameters=sweep_params_2d, plot_measurements={'x_fwd', 'x_bwd'}, axes=ax2)" ] }, { "cell_type": "markdown", "id": "157f6ece", "metadata": {}, "source": [ "Now that we created a two-dimensional scan we can use the `plot_2d` function to inspect what it does in voltage space i.e. without a time axis and compare it to a regular \"forward only\" charge scan. First we create that one from the `linear_step` sweep defined above." ] }, { "cell_type": "code", "execution_count": 5, "id": "8c7802fb-bff3-4c4e-80dc-d9be15f217ca", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "chrg_scan = ForLoopPT(ParallelChannelPT(linear_step, y_value), 'y_i', 'n_y')\n", "\n", "_ = plot(chrg_scan, parameters=sweep_params_2d, plot_measurements={'M'})\n", "default_params = {\n", " 'n_segments': 2,\n", " 'x_start': 0,\n", " 'x_stop': 3,\n", " 'y_start': 0,\n", " 'y_stop': 2,\n", " 'N_x': 10,\n", " 'N_y': 5,\n", " 'sample_rate': 1,\n", " 'cds_res': 5\n", "}" ] }, { "cell_type": "markdown", "id": "5aceae43", "metadata": {}, "source": [ "Now, we use `plot` function to inspect the positive sweep of channel X which is highlighted in the following plot." ] }, { "cell_type": "code", "execution_count": 6, "id": "71dd0045-292d-49f7-90ec-124b38d53566", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from qupulse.plotting import plot_2d\n", "\n", "f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n", "ax1.set_title(\"Regular\")\n", "ax2.set_title(\"Snake\")\n", "_ = plot_2d(chrg_scan, ('X', 'Y'), parameters=sweep_params_2d, ax=ax1)\n", "_ = plot_2d(snake_step, ('X', 'Y'), parameters=sweep_params_2d, ax=ax2)" ] }, { "cell_type": "markdown", "id": "813d89e8", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "aa0df99d", "metadata": {}, "source": [ "## Benchmark\n", "\n", "When we try to instantiate such a pulse it will take some time but not severe yet. However, a typical charge scan has a higher resolution. The qupulse parameter evaluation machinery can become a bottleneck here since it is run for every of the 100 points. There is work being done on optimizing it so see for yourself what the current status is." ] }, { "cell_type": "code", "execution_count": 7, "id": "f7213346", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Elapsed time: 0.008839400000000275 seconds\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Elapsed time: 1.7463892000000003 seconds\n" ] } ], "source": [ "import timeit\n", "\n", "# Try with different resolution by yourself!\n", "exp_params = {\n", " 'tx_sweep': 10240,\n", " 'x_start': -50e-3,\n", " 'x_stop': 50e-3,\n", " 'y_start': 0,\n", " 'y_stop': 0.1, \n", " 'n_x': 100, # voltage resolution: 1 mV\n", " 'n_y': 100, # voltage resolution: 1 mV\n", "}\n", "\n", "\n", "# using arbitary parameters for simplicity.\n", "simple_inst = timeit.timeit(lambda: snake_step.create_program(parameters=sweep_params_2d), number=1)\n", "print(f'Elapsed time: {simple_inst} seconds')\n", "\n", "# in a real experiment:\n", "exp_inst = timeit.timeit(lambda: snake_step.create_program(parameters=exp_params), number=1)\n", "print(f'Elapsed time: {exp_inst} seconds')" ] }, { "cell_type": "markdown", "id": "69c86045", "metadata": {}, "source": [ "## Hardware limitations and legacy pulses\n", "\n", "This section is under construction and will give an example and reasoning why old pulses often contain\n", "\n", "`RepetitionPT(ConstantPT(...))`\n", "\n", "and how this was superseeded by the `qupulse._program._loop.roll_constant_waveforms` function." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.7.9 ('my_expDev': venv)", "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" }, "vscode": { "interpreter": { "hash": "0187fe0cb56dae19ef91679276d2cfcb20275eec37c3a24cf7a15a9f8efe135c" } } }, "nbformat": 4, "nbformat_minor": 5 }