qiskit/advanced/ignis/5b_interleaved_rb.ipynb

{
 "cells": [
  {
   "attachments": {
    "qiskit_header.png": {
     "image/png": 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K7KdEZsu7QvYk/wuyZbahTeNE7Q/YTF8+gXY5+vSriPjkPZAYM/2yHpv9pM2pQ5svlCDWSeTlgxgzkXdQVLYP0OGiDNpS7mHR5m44iMn2bRZ5x+Xg0dmSbMPuI8d60RtCf8Fm1tofM8FFvh+YEM1nYkBKspxfaq3Y7HgsML9bkPMsFsT6C3kjQIzxyDsnJttkdKgOg3ZNNCHidR2IyXasGXnlavDobEm28iIWOYYSW8Fl2MJcvW8QBS5JOIuIL1aBdNR5ZTVmbLE/hgKYqUcO+54CRLqPvFQQIxt518Vk+zY6/AAdyLfgZT8Qk+3gJuTd8gPPzpZkqz5iL+JBOHD8t5kc4Vb9OFYDLvCe04RovZwEEgmd83utI1r9Fh1wAm6yyDmrBZFeSZLtYzHZLkGHUuhI4RkfEJPtgBrkPQoFz86WZEsl1iLH+ElLg4X8/clUe2nJABq6FPcbItbukIMEvIfuuNNkwVa3Z7SEusVoTySTAnEskmRbLyJbYXjDpTrdIuIp8qriwMOzJdkqStGmfjg0U/T72oCtrPrKX4tju0qFKahGxOvDoLs0mesv1hpZbFX3aT8FNMuoRpuNDIjijZJki7SIbA+gQ6RE2QZUIK8+FTw9W5Ktz2l7n5fU/PdMuog8s/7Jb+8mqaEzfb9GxIbO1nm97KAzutFbymsNVnQwnRingVbMSQtyrvqDKOESZesnIttf0WG0NNmqypFnGA6eni3JlkqpRY5hLfDogOK/UcBseHVh5+S+jHOIArLRTxGxYmLn1ToDIW3avLKKemGzaD47zY8G3vsGe8pDaRAjQaJso0Vk+xk6LJEm2/3Is0wCj8+WZKtYhDav0kBIETT7vAUFrMbGJye3FQzwea3BPnZeoTtt6Zf5dZVt89V2wH3LFi8dkvXe11erDSYWBeoP5vvLQah/JdqsYcS1J1G2qSKyXYMOP0qS7VIUmAMkW5Kt9xF7lReU4EyunfBzDTqxGJpe/v398vGxPv/5Tx8bPkQq6w4iPpwF7XOq3BH7fxQhg2fvPH6/1mC2opNHe4ZpZeCMPmpBznkNiDFcomwzRGQ7Hx0aEyXIdpwVne7fJFuSbUwlcgzroQ3aJ3X1X0Z0xpr1jVVXftsya2hfb8rLzl5iwCoroulYaFfZetlRqpCMgmXfnv2n1mC0sOis+khxtIoGAeEouS6FAhFSJcq2n4hshyDvH/9uZxtf99peSZItyTbfXk39MGgP4z9qx3UTvo61GAx1z68f+WL1rJyUcB/aNtZNvICIzxZBJ7jLvEMTRsxY9slvfz2u1evNFmyj+vfSFI0C2pNcgzbz5CBCjETZhorIFp4h71xMN7PVViDvayDZkmxb/5ewN7XQLkrGhI/bfKkO28GaTQZ9Y82jmxd//XLr0tKfWETLpTRfH0ZBQTOaYbx9A0P7xqWOnFT0wYZPDpy+8ehVg95gMrPYnsc/l2QEKGQUtEt9yYqc75UgQqBE2fqIyXYPClj2xXcnW+oo8o7SJFsOyZa+jBzTPmiLVqo1Go2PWqkKHvzuj3easAOs1WwyGgwm5BgfXf/70oVTx+1OnLl06a8bFQ+f19Q3NDUZjEazhcWOvDq3dXKcVqlS+2i0GrVS5gVt7DIi504QiKCQJlsriMk2A51Yz64fpXE32y0ocEVGsuWQbCNqkKOfC69hvAN0QUGBfhqtj1qtZOTqiGEL912vwy6xVqvV0srKYbEr5qendxQm+zMKRqX29tFo/AKCgoICNAwFziY3IccwmAIRDJJkWycqW/gOX2e9+tWHeTG06Gyno5PlJFsOyXacvaraJBDwUgWERUZGhAUHBQZw3Wq8vVUMw8hpRpcybdPhe3UoHePzP75amBOjkdMKhlHaotVqfAMCg4LDIiL7hgeqKBCIeoY24o6UqpQk2yfisg2qwXYZrn6xIIMRkW2qHp0Y+pFsSbYAq5pvBVq+WSYwOr5fbEwU160uKMBfq9VqfLzVKiVD0wAyRh2YMmn5vvNPGljsFv2La79tmzs8WssoZF5AyRS28bGtWj//wCBdWEREVGxs//gYnQocmAtW5HzCgAh3kZcNYoxC3i1x2cI0FjtkOv1usIvZ6h7ja/6gSLYkW/gNOeYfoQWliUpKSU4cOCA2NioiPITrNsDfV+utUshoaN3ZRMkYpXd4+qSSHT9fvFtjFJmvqfaf8oMfL56aHe2vZmQ0/wYikClUaq3WPyAgSBcabq92YEJyalIsv1fqEyNyLmpBhD+RNxHEmI68SyKzhXksdoK9UEi7kG15ObbxAcmWZKu6KzzTXB6WnDlkUHpaStJA7n7bNzwiXBekVStkjmIFKJmcUWlC4gZNmLNs+76jf9x6WCncUCxkNdS9eHznr9/3f7Rm3qTMfmFaNWMLludol5LJ1dogXTh3r42J7TcwISUtfVBmZkq4HOzm6pHzrC+IcBR5c0CMEuT9JDZbeNeKnaqYTnWRbfuaokm2Hp9tTANymsbZo43MzMkZMSxrSHpacuKAeO5+GxqgllPgXGx7+dpmpb5hcYkZ2bmTp89774OV6+3WLil5r3hG/ujsQUlxEf4+ahXDyPlc22cbL6v9Q6Ni4/rbqx2SNXRETm5mtAI4Q2qRY8miwXUbkbfY7cPjVorOFrLvY+fKw93JFk+TbD0+29EscirjAGR9c96aOG5U7sgRQ7luU1MGRgWqbLdZEIGiKa8+IJMpmBYKhVwmk9EUBWJ4Aa0K7DswOTU9Y0j2iJG5o8ZNnJwTLQMIe8gip0gOrstH3mYQYw/y8sRnC6rdJuxU1UgXs31exCJvHsnW07MtQQ573RvCxr1dWJD/1oRxuVy4wwYPCPGhwQvE8qLkcpWPigK38eV6B/fPGMZFmzs2b1J+QeHMcZFAN69JbWDAdVHI+wrE2I+8cBHZ8kI312BnLONdyrZ6IOxFXl04ydbDs92DHPMvqhHvvlNcNLNwSv7EvLwxWXEad8OjlT6+umA1SMM7asjovLwJ+QUzZs4ufufdXNWXJuTsV4EIgnJugBj/CMIBUdny1IV772DH6vu7kG1DBoDvC+QdIdl6eLZH7dmWTV9aurBkfnHR2zOmT84MlYP7VIFhUf3DFCARefCgidNnvF1UvKBkYenSwr1G5JR7gwin0IENcPMd7E+Jz5YXMHFbuQnbd1/ZZbaGkcCZhgKFJFuPzlZxFTmW31esXLZk8fvvLJg/MzMIukUWHJ80ZFAISMcvY/q8+e8uLF2ybOWKg2bkPAoGEbYi7y1wXSHydriXLU+VPX/3iccstlHaVbbmCW2fT1KtI9l6crb+j5Fj/nX9hrWrlixdWpIbBN1E+aUNHf3WEA1IyDe7ZOnS5avXbVi/39wy1xNhKvJ2g+v2Iu9t97MV8h5Ssq8anVR5d54tW9je08B+Itl6crYRjc2D5O3bNq/fuG5qGHSfKnnitOLiZBqkFFKwZtOmLdt3fG9EjjGTcnNN6rp7ByyzkdJkayMbdRaFFnSebQm0WoIC+SRbD842iUWOoezT3bs+WplGgQTo8JnvLds0LwKklbzs448++eyHRrQZR7v7XIIMt47FuAASZMvLb0TewU6zXQEO8uvIq/Qj2Xputjlo0/jL119/+24gSEPz9sadX32TrwBp+RV/8+2+n2rRplDu7m6nMrd2V82XNlsYjrx6eSfZbgOBwVbk7SPZem62+WhTf/jnsqlykAg9as+Bo5e/HQQSoyft/+VwDdosVIAIASZ0sEaDawaw6GDylzhb+BZ5KR1newycfIwCY0m2HpvtbLSpO3V0KkgnduOl68+q1mpBahOPnmrOdjkDYvyKvI/ANV8h7yBInW1QnbA/V5+4p32OvCcakq2nZvse2jSULwAJyd6+2mDGCzkgudmXatFmKyMud+Tp48AViUbkTZE8WziBDjNdzhbyUeBzkq2nZrscbZq+VIKU+h2w9bHZD6Qm/6gRbbaJy1b+CnlXFNA15S3k1Sulz1bQZqnr2cIh5LHDSbYemu0ytKkZBpKiZt5HxL/zQHKpL9DmS6XoSHjboWufosA2kD7bYnSYLyLbiEbkPVCRbD04W+sVNUgr7HMzonlPMEiNOW9Fzrcis9XVIo8dJWpUjZUad7IdUa7renZiN0lEtrAIBXaSbD0z2+3IsRwGqb11DRFvTwHJlZndyRZKUKBuOHRuTCMKzAI3si0y4TVf6Ng+dBgsJlv6CvKsg0m2HpntOnu25yiQWOAOA6L16yiQ2jGLW9nSV1HAOAU6U2RGgXJwI9sNXb3Hw210iBKTLaRZkHebIdl6YrZL0eZFAkht1CVEfDSbBmnFPEObr5Ug0mAWBdj3oWMrUIjNEJ8tcwDtTjHQAa0VW9XIRGULu1BgA8nWc5ekTJ+B1DTrGhFxf3+Q1sdGtNnOgFhfopNjA6B9aWfQydcgOtvAS9jiRiK07wd02AfisvV5jDxzMsnWA7NdgnYNM0Bq2acR8XmJHKQ0vRHttonPNuAlOrHs1UFbkd+z6KTCT3S28Q/QwbiYgnaUIm+yyGxhAgpclbmT7TmSba/Odh42q8wDiSmX1iDi4XSQ0LhKbLaaAdHSG9BZw7cFGueyCw8Y0FllXxCbLV2BQmdioI08CzoYvMVmC7+gwAp3sv2CZNursy3EFlVTQWJphxCxZokSJFP0ClssUoB4Iwz4OtPJJTNyEoIUusTcwpUXrPi6hhQQnS0k1qAQe6pQCUK5Z1FgG4jONrQeecb+bmRbig5ly3lToFcg2eZhK/1aNUhK/n4lIp7MBolodxmx1Sw5uGGSBcUxjwI3soX0enRW9/nUrEg5AKjic+ddRqHnPuKzhRIUKKfEZzse23UMegWS7RB0sJzMAEkNKEPExnUakERuuRUd8mTgjtkozixwK1vIbsK22Mqbr7CNQnAjW6ocBRaJzzaWZNurs40zIa9mUyhIiJrzEBEv5YIE4j83IM+cTYNbSszoutrJ4Ga2kGtE15SBO9lCkhl5TTGis4U/Sba9OdvgKhRg7y0KAulEfW1FNGwPgO6KWP2CdQoqiQL3ZNxDV/3ZF9zOFka/RFcclLuXLWxFgTPis023kmx7cbbeFejEWvFhOEhm2h1EvDYRumfgtqdWdPI4HNyl3ouu2aWAbmQLuoPYtRMMuJmt6iEKzBedLXxOsu3F2cJ55FiuPcZW7LM9GTRII+QTM6L5szBwnzp33ysWW909bUbOVR9w38SX2LUXEwDcypY3ux47x+5WgrvZwlgUqA8Xna1/Jcm2F2f7A3JMXw44jjzjqbkRIIm8K/ZHy4GbqITlf5mQtz96txE5B1XQDbrttdi5+wuU0O1sIfJs579INoD72cJ+597EZgtBB0i2vTfbVcixnAPlgofIYyt/nBIM3ee/1YDI7osGN8jiSk7Uo8C1KTI4ZUHODga6RTX3GnasPJ8CkCBboAo7/mVerFdBt7LV1aLATJHZ2kx4RrLtrdlOsVf6UAcQuv6RkRWU+7xs3gAfRgbdMvwci/hkgRzEoBTK4BFrL9Sz6GA13FjkDxB4l0XOAgV0V3ZZI7bDenVPNvDczJaX83MDtuPMFAVA97KFeShQoxOfLWjyFn95sZpk2wuzTTMhp3aIrZb47Q8NVhR4eXptbpiKocFt3itqEPFgshfHxWQZbdrcb+6YUMBiuF4aApykantbOTR0H9VvxvYztcgznFk/SgNSU4zcfqYSeYY/dk2PBHcRJFt+aUI/G+xiV9/Sm1HI8vC3Zbl91YycgnZ5CUFbg46yiFWlSq/XQVsyRhmUPnfvX3UsCrCmpgslgWA3XY+cV3EgmfHo8Dn0GN+M0flFJXMmj0yNlgPRXSRb+hxyjLuhRdCsYzUGKzqxPP5964xUHdcuLYiV+6yPF6cPp+MUmUVViHgik7vOcWGfNvVSNKP07TduedmdJnRmMTzb/5YGWmw1IudvX5BMqjDb3oEg2cKn9jjOyPmQ0zdcaTSy+JrGO4e3zMyO0KpsN94+LcV6dd1t4k8sYu0aHz7bPvxroQ/QCqVPcMpby74rr2axTbOvTi/qJ4NW9DELcvYpSbaejWQ7C22eRICAduyntxqNVmzD8Kz8+7Uzh/ULUMplMrCV2GW2suLHiHguB/ibLfeK//TxomkZ4xc7ZMqyL049qLPi61iz4eXZ5RkMCAQ/spe9UEGy9Wwk20QDcprywFnA6J3ltQYzi+2wvrxz5uuVc8alxgR6q5QKhZymKQBhtEJx+6yI+i0BtqopSqZQMEq1Ljpl7OxVe4/feG7E9liM+keHPkhlwFluA3LMOTTJ1rORbL0fIMewCdpQp5X+9qDROV0h1vjy9sXDX20qnTVpZHq/0CA/jVqlUioZjqIVwzDKwgpEvDItKCw2dVhe0aJNXx2+eLtKz2IHrEb9y4t7Cvq20+YqA3IehYNnZ0uQbGF/84YLBbSDCh21+sj9JoPZip0yNdVUPrh26fTBn775YufmDcsXlTb7cO2Wzbt/eWyLsfJ+ZXWDkcXOsGajvrL805mJKmiP/IQFOYdUnp4tQbItQZsX/aEdXgC0f+KEVfuvVjYZTFYWe4zVYtRXVxzbNTc7xp+hwAvaEfUMbZYqQDopJFuiN2aboUeOfh60RfmERPYN1/lptCFp+at+KH9arzeYLdLGy3LBGhpf3jqya/bQ2OCAwLCo2Pi4MC0NbRXqkWPMpkE6gSRbojdm6/83csxl8Dp5YMzA/rHREeGhQUH+PoxCGRibW7y57OLD2kaD0WTp5r2XZS1cr031T/48uHvhxJQwrcrHLygkIjI6Nn5AQkpy/xAlBa/53oScm0EgpVfY6tJCgfFAEG9wtswOe0VPIkGIUoUnpackDIyLjYmMCNUFBfhrtSoF9PECJiRpwvvb9l+4/ayuUW/rV0zArNViMRkN+sa6Z7cv/LxncUF6hDcFXn1opdYvKCg0PCIqNm5AQnJa+uCsjBgfGoR091nkfMyAlC5gu34BgniDs6XHWpCjLwIBdezgYZkZ6SmJAwfExUaFhwUHaFVyCgRobUzGmKLlu/YdvnDjEbfa1NikNxi4ik1ms4VnNptNHINB36RvaqitfHLj/KF9O5cVjcmI8ZWDEC1TB4RExtqqTUrPGJI1dGTuiIHewnALmuzhT5SBlPaSbIlemC2EVCDH/AufiDxq5LjcnGFZQzLSkxMGxMWE+avl0CHGJyg6eei4KUULV2ze8cXXPx86cupcizPHDh0q+2bvrk0r3p8zNW9EalywDwMd8pKrdVEDElPSMzKzR+bmjs2bPLafGhx+MCPnQRhIaibJluiN2TIfo83zeGgmC86ZUjBxbG7uyKFZg9KT40PUNIhDyVvQjiL/83//BzLaC7oi84lIGJw9PCd37PhJBQUzZ02IlEOzqCcscvYqQVLUnyRbohdmS482IcewBOyY1KLimVMLJueNG507YnC/QAVIwItWqDUB/gy4QBGcOHzM+An5BdNnFs0rmT/Uuzn2EgNyLJNkIK00K8mW6H3ZQuA15FjLfWx9+U4sfX/+vKJCLtwJI/trQCKU0i8oNEonB5doE0blz+CiXfDeBx8unxpKA4DyrAU5t4NBap+QbIlemC2zFh37kkPmrl7y4aL35hXPmTkp0Rsk48XoouISEwPBRT4Jk4sXvL/ow6UrV298P4oGGFGHNlsYkBqzwUSyJXpdtlRKLXLMZTSEfLB1w9pVS5eULpybqQUpyYIGpAwZmaoAV2mGFH+4YvXajZu37l4RDdRX9rYahlAgvcRyki3R27IF1c9oU5WsWfzZ7u1bNm9Yt7wgDCTmmzRibH5BMLguZPLqzVt37P7k86/W+cU+RZsTPtATqGnbDlaYSLZEb8pWNsmMHOMnM3/86vPPPtmza02WHKQm7zehsPiDMWK6k/8/e/fh0NS993H8H3nuvf1mgmAAARdOcNGiRdy7tigWtO7Zwa3bqrUW9x5VXGC0roqrqFVcOCpOlkzDDPOQ9XnOyCEhhhDUXPTe8+o+g5P8ct4c8supDl63/7fEI0nHTy/c3gCWeaaSPEbRI8ymM0kkH3a21OEBONVXTp86eSLp6K99yAOCv1qyetOm/gpqhZ5rjp88dfrMuT8rwXnekQQSiZStKt4MliXn0uVLF85v6UqeoByyZteh86taV16nX1MupV65km0By7JKTRKJlK1Vl2xwmIe3b9082p08o+vikxfTH072otbovC/t9t2HdeAU9CaRRCJlq14jXM7KHj+6PZY8RBn9x5OC+vNh1CqRqRlPXguPbquaJBIp20bdcsExFeUekJOn9Nhb2oDauT7UKgnZhQ3g6PqRRCJla6NaZgKHKYklj5FNfQ4g7TNqldG59eCYN6pJIpGytRPyHLyqSPKcjjuNgGGlP7VG/zzwXoWSPYlEylb1gwGcuq/Ig8bnA3gcJaNWGKEDx/iTmiQSKdsmgu+C07BPSZ6jSWEAy4Zgcp/i12pwMrpSUxKJlK0irh6coijyoOnZALLHKMhtEZlmsJh5SpJIpGwd+J62gGW6HkKeozleB2B/CLmr83kGnCv+5EgikbKVRb4Gx/hHiJw8ZuIzC6CLVpNb5CFJ9eCUj5bTe7IrOfkr+oCMWXssOTmSWiMxOXkUuUO5IGm2jDhHkpOHk6OOycnJIcQZeXB1MP2PGfszO/Cff+zZknqtQej2wgA1eYrX/ioAx3uRO9SfWa+1xu1qegvB3ZroSpwCYAV9OJLAiaPWqAYWkjsmAxhNHAaYQ45CAXxGrHY1wF7635IMztcffbbU6RZ45owpGhl5yNBHFkA/w5taJNdMyzCBl9GN3kYamnj9AWY7E57MNgHAcjeyjQBwj/6nzMJ/S7byCZUQVO0IbUeeodhYDuBSP2qJz6f7qiGonqL46LJd+uDBTGrZVWDToEGD/DyTbRSAfm5kKysC1tBHoPuDB3/Qe/EXsNHlwD948EDxUWRL6s0NEJif/BDioXDD75iA+u/bkysyn57LMk0QGA+r6W2zNTM2r/6T2e5y7zD5wLdk856zpV5zu5Eb2ZJm+lD6GPQFcui9KGxpDAGoPo5sqWOaBVam9G+7tpORByx/DeBOBDVP0T50WYYRor8H0Ftne5XsfXjZ6j2arY3LbFvBb8a8eR3+G7Kt/i/KVj68CI1Md37sr1HRe9ct1QAYVwZQM9QBkeufG9GoLFb9EWa7978y26n1ACKpzYT/x7JVfETZkiq+DjamrB3jgn0U9J4tygfwZKharXDSrKbntGSdBTbMr+3Jo9mqyYl2dutlJHK5i6zJ0keOh/F2L1uFwv1TTi1762y9W8hWriZH3jKii3Carasfy5RKcpMPiZodt0VOs3X/9XM/2+Fvk626jbIln0QD7FXfWDU0yEdJ71PwOQawHIiLHhPWQdXkvNeETNr51Ah7xvOdyXPZDtyUWmF6emyRkqy0Wm0U9fy9EjU3pxKrd3KRpS59nZIaef87+aW59EpCH7KaqdUmkCr+hcGceTKUeLtP5gJ4zH6xL4mjWXu/HOw+46mJg1qtAbjPbmYtYcr+dKY+ff9kEiVqtWOI+i757egbp1yvAzpU/7W5Owm82a8SToJorXY3cXqxCwPfyNZ7VYoO+gsj7bLdp9VOJM4PWu0ydoMco/HZsS7UyPfXGzqY8hL11+Lj44PInmbJIz2YlxeiyYmIXXcZc/7mYJrBDRJnsla7k6z2iuNDFLYnh4H+0dFQsurMPvQQirqsR9k16yOfpL1pBGrYFTuItUOrFQeqP7vMl1gyfix7n65EzY04YoUeL7LU3lurIJE4rMbGge/J/kN8Sgu12lVENFB7uR7A7+waOREt02rjSeDHLgojznatdgpRj/i9WrWwZvmpHEtxys8hbZEthaSZ0YSl8uqqkZ00ahm9N3GZFsDUYKzLPTreX0Y8RbuAPrG7Mxg0ZcmIkHsu2xVGCO6Jgw1gWpdy8I6rKNoAXnpnsur/HIK6eSTYAdynA+DVziJOHkTLifWVHlaJarJTAVEcsdqfgFWyr93FWHnEDNQ7ZjtcD17ZCGs9ACaQYBWQT5zBALo5Ztv1b/Asi23ZvgaWEucEkEJ/gFfRWGKcDlZnyMGIUlilhZCj6Qx4+aHbgQfEWWN3vSwClhHvJzMExr3yxnexCI8ygnfah98Iomxi5dhmv8cC6CD+aBsbUglekpKmGMG705Hs6SH6migSQA8SHAFSiWgSGimJKAU4QYJOAIYTJwv4mXYYAfgQa0g+BJWT2iJbWVQuHFmq722fOiDwvaXre6IOVrq1ITJSevt3HfrjqTwT3lAYrSZPZau8woV2+2yWBaiMasx25h0w+Uaw1vVrgCm/DqzTJIitByzZZ+/UADhoy3YaUKYDyxj6ZrZhVYD5yVUd33/HZrPtkQtWQSFY2SG2bI8Db2a7VAe81oNlmNK6bHvrwGLyTbDMdJ7tYkBXBlZNEPE+NwLlSQl7ud5/dKjWCKAhPcsA4K6cmloGTkkZ8HCHy2w3g1V+txisNbZsR+WDyTOCtaEV2U5P558da024QXz9Tnok2x2ANdvvjID5xen0egC/tEG2pIgrgxPmwtSEaeEd/bxV795uYHINRExCaI+R3yWm6+GM/od25LFs4wHzFhURDS8AHjdmm/N4nIzUCxmg5nrJXDXJhr4CLAOFOMqAjHAi8kkCMEbMtrQyIZgo8Ih4OeoXEaEDdkdERAQR0SngVV8i6rnKiOs+ZBMeEVEDbGQ30xDReYBZpSHSrGGA043nVxJQlH7xjEO2RaXxPkRdd1uAXEWrsk0GcHWAnJTRGUVOsy3Qr/In6vIHxDunlJnAFV9ifQuYIsneQyBtgjdR8AmIDTYKqAUaVgYRdfilrshVtiFGYM9AGVvQM8DYrzHbnL+GEam+0QN1nYgoOCJiP1DIDldfV9nmPOJev28ZoOqGbraKZMPzAcun1HTga4EEYeCdZKuJiFgGIJLdgFxkqzVBd//SBTVRVwa43YOIOlwAzP3bIFtSLa1ubNXSNN2SW4k/TAgL9vNVy+ntKVbo7Mu886LK4TAGWNVtbE/vlu3DUTYO2bYvAxYTr7cBmNmYrT9xlgAwCLH2tQDbiLMJyFQT7yzwUMwW84l3CbB42x1GkAfEEm/cCXWzU1IjActw4o0GMExcjWv2oYjZ6vsTby6Ama3JtrsROEo83wyn2eLLxiL1xBkIIJh414B1ZK97VroPceQZb3yPTADMw4g3weQqW4phNhMvrAGIb8z2pkpYC3GhbW8X2WZpiLMcQIMQaziATc1MSTnNljVFnJJykS3SR9huUb0jI95d4GJbZEteW+vAM+kKqwxwwBTcPrI6NrJnkL+vl1ouo1aT971nRrMsTMUL8R0ukxhI75itPYeefgEy5LaPazLFbDcQrx1ju+HvuvUqGswA40jQ0wJMtGZbI7f11s8xWy8Ag1r+ACgdSCKrE8BtcfUjNTnJ9qDdc8xoTba7gcoAEkx3mm2e3Vp+ywVAIQk2v3FOBvmTYAWgI3tqPZBIVjddZkuhZHUbSGzMdoYt0J3uZ7ueeL4G4JZtkH73QLa5viToa4ElnATDAHzaFtmS5jAjZPtk8dpruuoGJ3FVZt5IWr9wUmRo18AAjY+3WiWXkysyuVLt5dM+ILBz6KANOjTDzFRmn1k07Z5RqPZsV/JgttfEQllx4mUSwHQSZANHSLAP+JtYE4E6GVkVAmus2T4kQTCAaMdsSbxz0GW2ajMwiaxiAaNSWO1s12pxJ9Z3gEnhZraOZ3Afp9mKXYaLn/UkAM9sX/kJOTcFQDvHT4VHk9VOl9naHAOuN2Yrnv0XgBT3s40jQZ7tu8ZB4L4Hst1NVguAXLKSNwBz2yRbCjpt7fbhCM3wdVfyK+tNcMaoL3iYqt3508K4L4Z9GhrSMTgoIMBfo9G05/i2Z//NLyAgMDi4a6/+g8fFzFm6MflS+qtKI5yxNFSXZiTP79suylptw5+hsnfOtvaljUNPBbZC6VMA4eJMsu0CcZgEa6wdLBYL5fwFJDXOJFsZgHlvZHsGMO6NUrnOtr94oRaLCRNXu/7McZRQptvZ5gPryaq5mWRBAICJ1u8MVSTYD5wg5yYCaE92xgPoTFbb3cz2AHDTNpNMgt+Au+5nG2u7cB8kwS9Argey/ZmstgIpJMoENrVNttTlnNCtOTO2HXmHLzj6SKdnTGiWhdGXFGQ9TEs9d/Lo/j1b1vMSduzff/jE2Ss30l/kv66sc7W7oa4iN3VDdGcZeUc/FzY03OgjVuuRKSlvC5D/wiobQJxDtldt2a60drAfqH0hqgHuOWZb7yzb7pVgGV4mze7cfLZfA9CQVSCAaPey7QtgpPvZKkzA9+5l217MNhzAKOJ4Zb8ZGo34YcPR8ykp6Y7ZLgKgcDdbr9hl20+mpKTkO8t2z1tle8OW7RoPZ3se0L8QMcDZNsqWnUhkhKCKF7YnIlnQiGXJD4sr6xrMeO9qy3Ku7Z7VV02s9nPzLeA03I6Qkyez7QsH8S1n+yeaeuVWtjTkGQTGrd7NZbvCPtsAAEvcz3am+9l2BfBdK7OlVKA4htv+MlDoT00seA6RQ7YJAOTuZavZUgHRx5jtEzR1u62ypc7WblH5S6CMeAGD5+++9EJXWcMYLXh/6g/GiP+boMz/p3LwmJtitZ7KNgiAdr2dyJazPQ7krrez2L1sST79WA54qc1lO9cx25nuZ/ul+9n6vE22vqkAmOxyAOZRJBJHBob7F09otTcds13qdrbKGwAqbp3VarXZH2W2fwEZ6+3Mb7NsqfPZemtWSb1UJFJ1GTZv07n0vFIuXvPb1euw15NPyUrZI7HOWu2lPjLyXLZiMV+TI9fZ/gzcIXtuZCvSRC3PBTC1mWyHAAghq54APncv288B9LNm+0XL2VI5sKy12ZLyGASOvzb9EAuQFOj8ve0UcYFjtrmO2W4ETAtUxDnQqmzXvq9se7aU7UmX2R4ATpO9tsuWgrW14BlujmxHTfiFTfh+S/L1jDxdub6mjjGa3OnXYjY21Nfoy3V5OgNsGjYGk8B72PUG8OrOhMrI09neB9a0MttpQMVbZCvy0QMHmsk2AMAY+9kcjXvZzrDepqO2ALPdyPYhkNjqbAen6PccO7v7u2HkYB2gk5HzbCMADHoj26VAtWO2j8TH1KpsnwB73ke2AwBEucz2d+C6y2yXAk8/lGwpeF8VeOa8b/2cZKQO7DcyLn5j4umrD14W6MrKK/RVVbW1dUw90yBgGKa+trZar68oLyspznmclnJky5Jvxgz4+m8TRMa0gTLiyDTzss3gVR/qSuTxbI8BqST6zK1sw+2newNC3M1WTlaHgNRmsiUdsJJsp1khucz2V7LaD5QQp9h2Gu1xke1p4KmMBKPdzDYRRRpy6gKQ6nhxFflZ7G6F/F3MNgaAP/HUjJCtygD8QIJj7mebYnuos94l23Z2r/mfTrPdZDvqIKfZTgAagsmqa4e2zZZ8/51t7av+1AgNNUvt3yNy7MTJsbMW/bD8p4Qt2347xDu4a8uWX1cv/2HBjNhJE0YN+ax7ezkJYm6KN0bp/xyuIo7P8OR68MyvVmjoP5BtuBn4Uhx34wl1C9mKy66TwPueLtK9bL/KH2b7JGNfc9kuBSoDiRdcBfzbdbbP1MTrYwA2iU82Uy7ejNh8thMAzCfBYfey7Q/sJedOAX+TYLtjtnQOKPMjXlCFmG04Gu9N+xJCtrI624dSWe5nuwNgOhLv9LtkSzrgMvH8apxmuwDAEOKtd5qt7AmQTIKAzNywts2WvKY9YMAzPvm+m5Kc+0QV2DusW7Cft0JBn/zrH//8178++eQTIvZv//rXP//5j39+QkovTWDX0L6d231CPNmII09Lq+tqSp7vjVARS9555iMjeMzfs73ovWWbFmwTRJxXjT0dBcrjiDO2FkhoKVsx9WQNsQIuAmVezWeb23iYBWYw3xCnZ6WYjJNsVTnA3VBihaUDmUrX2ZovdiLWkFygXnheGwBsU3C5/W56M9taMVt6BNTwD8J7g9m9bMcBzIIhPbp3795FQU2tBUzCCT2p7o1sBwG4LzyjR2YxW3UJUDqYWJGlFiFbegBk8bvK9xpcZ7vSLtsYABc0RKRca3qnbE+L03TBt02N2UbbZdvHDDwOIdaMBqfZ0ngAu7yI1Yl78WRtmy2pRlyohEB/ZESA82q9eg4dMnAAe7eFf/t2ShnXK4uEbj8hmdLL1y+wa+/+EVHDBnhZu6WAib8kJiWuGeNDHM3gnUXiYa6MU9G7cf1LwF0DSjMzOxNRZz2Ae/tWnCoD8ErjTraUCEB36udtf1sghuA821T+MJm9iMJyARSfiv9+fy2Q6dNctjTZAjAXN2+5xACWr8h1trtMTMq2fWkmNN73p6kA8OLQxhNV9U5+SH4C6DIzvbjDgPXk2MaTOmxzL1tfI0Tm4ssjyE5fBmBSl8YsvoFV6UBWZgjZuQyg/tqO3anmiiNitvQdAOPlbbvTcFx8b7sEQOnxmd+syzNPMKI+81Kz2X4NGNhRXS3eDopi7YbDBdjyTtmGGgHc37/lHFN4ojHbPgDYIx0Rv8FDf2bjgWfY4jxbuggg//jqPc8BWMZTG2dLst57Ci3gmZ4tD/WmNynDYmK+GBUV0bdHR39vlVxGYprEp8tm2z6oW5/PokaMnzwlzC5JlZeSeF49Ft1hICg+2EdOHs12GiCezD3uQ/SwO7mVrWyNCVbMImouW7EO4Z1w4B2IqvpTs9nS+BJY6cZSC9kuTIDVXgUJZpsgWOxkSurf4HgTaw4Dwc2+7mVL02pg5zDZWQarjHYzwOpFdnxTILDM3t6YreIWBMUhYraym7DaSacAvGg2W+98cHYTJ1IPwfGx75Qt/QqBKdo2JUU3AYhvsrrkQ3C3RzPZqnZCVBVLbZityO/HpwwEzOVpIao3wu4yI37e9CkTR0eGBnk7TU7GvvcdOPKLydPmfDe3m/yN6jtFayshYJ4t8yfybLY07ykjnszKzfngZC5TkVvZsoalM2DVJw8gV9nSzAzGmi2pN74Gh9kVRC6ypeAzerD0p4OoxWzpx1KwChZSo6hcsEoWkZNsZUteGqzZUtQTMwDmgNrdmWSfFEsFrxacWWQnhj+o8YSGFBvzLdZsRbKEUrDyYsmWLSkSTGBdDyMxW/L6uQ6s8pXc/QKVLrKlnimljdlSyC2w6rcq3jFbiikHKyea7LLtcKSwMVvSnALLnKR5cyZZFP3YyEd7oAd9CNmSatz5UggspYfGdVJQE75fblm/etn3c2IGdlBQM+S+fSbM+m7J6vVbv9Y4rAkauf2VCYKyyxPV9B8WOH7BxH7UKsrP5kwd0p5aQz5o8sKYcB9qUc+42B7kHvXoudHB1ESPmDnDvKllvqMWTAwkd80vYWJI0G5KEXCd7Cn6RC/4shM1p1vczKFyErIVeUfOjenj8IgGfTNntBe1Vsfx88d0oHcn7zN1RqSSXPAfOf+LLuSSOnJ+7GBvog8jW5KFJGQxEKd5t43saJ/nJ903nzp2YNempUN9yQWvfvM37PjtyKkdoU2jjVr3pAECJmdzTxl9aCSfW3CcGk0Cyqn1bNlKPJ6tSB1zocQCgSlry4guahIpR1y/d+vqpeMzfVqa3NpzKTXtXtpYu12Do9b+zcCq9HKcN314JD8Ba+wiBvKkbD+ObEnWbd2TaliZsvd8GSIm5v1Nsa44P+9iOLWg48ZXBcUlr+f5ksCr65iNGQys6l6u7y6jD5BkBXBPbt/fCSnbjyBb8Wp5LI+xfQ5waGpYgIJYvt+ZzSaT4U4YtSBwB2M2m7FUQyy5X2jsgWwDrAyFpyep6IMkiQRwfqCaWL4jkgFMkbL9CLIV+c26XGyElaUiddmI7r5yUk+tB6sw3odcUo/7C6yGeT4k8wkZtvhSmRlWJt1f8Z1l9IGS7AdHX17OgLOEpGw/omxJ1nXJDZ0JIkN20qKobv7DXvLpPZ7agVzwGf47n/er0X4hn889/LIBIlPFvZ/7KenDJVn2EiLDH1+RlO1Hkq1IHrr6ts6IRsyL5PiZV4T6Mlf08aVmqEOmpTLg3Jy+6FBGHRqZyu5vHqSmD5skfO6yzYm71yyYFEBvp8vAgWHUJqRsWYq+P90oYGBjKBbvoqq5NH9AsJMClX6hMYmFEBTlN8CmQXd302AvEkkkUraeIe/1Q0pONZypTUuICe/m5yW3+119Ovcdt+z8awucqMtP/Slc7FwikbL1qOC4Q8/KDHDCXJF+ZOk3Yz8P78saMGhk3OI91wqdbmksf/n7gp4KEkkkUrYe5j0qITVbb4Izllpd1oO069dvpL8sqna+ianq1V9bx/uT50kkUrY2ipDY3TdzKhvQagZ97p1907sryfMkEilbB6qesVv/fF5cY4LbTPW6l1d3TQ9VUVuQSKRsWfKOoxYfvvmySN9gRgssptqSnNvJK8d3UVDbkEikbEWqbuN+3HMp/WVBWbXzeC2GOn1xdsb1Y2vjentR25JIpGxFcr9+4xclHDyf9vB5Zl7h65LSCr2+srREV5SX/SLj3pXk7ctiI7uo6UMhkUjZiuSazv2HR8/+ftnadZt37tq6fu3KH+Z+PW5Qj0DpGvv/7dOxAAAAAMAgf+tJ7CyHYGwLaAtoC9oC2gLagraAtoC2gLagLaAtoC1oC2gLaAvaAtoC2oK2gLaAtqAtoC2gLWgLaAtoC2gL2gLaAtqCtoC2gLagLaAtoC1oC2gLaAvaAtoC2gLagraAtoC2oC2gLaAtaAtoC2gL2gLaAtqCtoC2gLZAy1EI1wwY3vcAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interleaved Randomized Benchmarking\n",
    "---\n",
    "\n",
    "* **Last Updated:** August 19, 2019\n",
    "* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.2, qiskit-aer 0.2\n",
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "**Interleaved Randomized Benchmarking** is a variant of the Randomized Benchmarking (RB) method that is used for benchmarking individual Clifford gates via randomization. The protocol consists of interleaving random gates between the given Clifford gate of interest. The protocol estimates the gate error of the given Clifford. \n",
    "The method is based on the paper *\"Efficient measurement of quantum gate error by interleaved randomized benchmarking\"*(https://arxiv.org/abs/1203.4550).\n",
    "\n",
    "This notebook gives an example for how to use the ``ignis.verification.randomized_benchmarking`` module in order to perform interleaved RB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:57.016012Z",
     "start_time": "2019-12-10T21:49:55.010322Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython import display\n",
    "\n",
    "#Import the RB Functions\n",
    "import qiskit.ignis.verification.randomized_benchmarking as rb\n",
    "\n",
    "#Import Qiskit classes classes\n",
    "import qiskit\n",
    "from qiskit.providers.aer.noise import NoiseModel\n",
    "from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select the Parameters of the Interleaved RB Run\n",
    "\n",
    "First, wee need to choose the regular RB parameters:\n",
    "\n",
    "- **nseeds:** The number of seeds. For each seed you will get a separate list of output circuits in rb_circs.\n",
    "- **length_vector:** The length vector of Clifford lengths. Must be in ascending order. RB sequences of increasing length grow on top of the previous sequences.\n",
    "- **rb_pattern:** A list of the form [[i,j],[k],...] which will make simultaneous RB sequences where Qi,Qj are a 2-qubit RB sequence and Qk is a 1-qubit sequence, etc. The number of qubits is the sum of the entries. For 'regular' RB the qubit_pattern is just [[0]],[[0,1]].\n",
    "- **length_multiplier:** If this is an array it scales each rb_sequence by the multiplier.\n",
    "- **seed_offset:** What to start the seeds at (e.g. if we want to add more seeds later).\n",
    "- **align_cliffs:**  If true adds a barrier across all qubits in rb_pattern after each set of Cliffords.\n",
    "\n",
    "As well as another parameter for interleaved RB:\n",
    "- **interleaved_gates:** A list of gates of Clifford elements that will be interleaved. The length of the list would equal the length of the rb_pattern.\n",
    "\n",
    "In this example we have 3 qubits Q0,Q1,Q2. \n",
    "We are running 2Q RB (on qubits Q0,Q2) and 1Q RB (on qubit Q1) simultaneously, \n",
    "where there are twice as many 1Q Clifford gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:49:57.023478Z",
     "start_time": "2019-12-10T21:49:57.018667Z"
    }
   },
   "outputs": [],
   "source": [
    "#Number of qubits\n",
    "nQ = 3\n",
    "#There are 3 qubits: Q0,Q1,Q2.\n",
    "#Number of seeds (random sequences)\n",
    "nseeds = 5\n",
    "#Number of Cliffords in the sequence (start, stop, steps)\n",
    "nCliffs = np.arange(1,200,20)\n",
    "#2Q RB on Q0,Q2 and 1Q RB on Q1\n",
    "rb_pattern = [[0,2],[1]]\n",
    "#Do three times as many 1Q Cliffords\n",
    "length_multiplier = [1,3]\n",
    "#Interleaved Clifford gates (2-qubits and 1-qubit)\n",
    "interleaved_gates = [['h 0', 'cx 0 1'],['x 0']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Interleaved RB sequences\n",
    "\n",
    "We generate RB sequences. We start with a small example (so it doesn't take too long to run).\n",
    "\n",
    "In order to generate the RB sequences **rb_circs**, which is a list of lists of quantum circuits, \n",
    "we run the function `rb.randomized_benchmarking_seq`.\n",
    "\n",
    "This function returns:\n",
    "\n",
    "- **rb_original_circs:** A list of lists of circuits for the original RB sequences (separate list for each seed).\n",
    "- **xdata:** The Clifford lengths (with multiplier if applicable).\n",
    "\n",
    "As well as:\n",
    "- **rb_interleaved_circs**: A list of lists of circuits for the interleaved RB sequences (separate list for each seed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:07.505751Z",
     "start_time": "2019-12-10T21:49:57.026615Z"
    }
   },
   "outputs": [],
   "source": [
    "rb_opts = {}\n",
    "rb_opts['length_vector'] = nCliffs\n",
    "rb_opts['nseeds'] = nseeds\n",
    "rb_opts['rb_pattern'] = rb_pattern\n",
    "rb_opts['length_multiplier'] = length_multiplier\n",
    "#rb_opts['align_cliffs'] = True\n",
    "rb_opts['interleaved_gates'] = interleaved_gates\n",
    "rb_original_circs, xdata, rb_interleaved_circs = rb.randomized_benchmarking_seq(**rb_opts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we print the circuit corresponding to the first original and interleaved RB sequences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:07.566650Z",
     "start_time": "2019-12-10T21:50:07.509403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ┌───┐┌─────┐ ┌───┐           ┌───┐┌───┐           ░  ┌───┐           »\n",
      "qr_0: |0>┤ H ├┤ Sdg ├─┤ H ├───■───────┤ X ├┤ Z ├───────────░──┤ Z ├───────────»\n",
      "         ├───┤└──░──┘┌┴───┴┐  │  ┌───┐└─┬─┘├───┤  ░  ┌───┐ ░ ┌┴───┴┐┌───┐┌───┐»\n",
      "qr_1: |0>┤ H ├───░───┤ Sdg ├──┼──┤ H ├──┼──┤ Y ├──░──┤ H ├───┤ Sdg ├┤ H ├┤ Y ├»\n",
      "         ├───┤ ┌───┐ └┬───┬┘┌─┴─┐└───┘  │  ├───┤┌───┐└───┘ ░ ├─────┤├───┤└───┘»\n",
      "qr_2: |0>┤ H ├─┤ H ├──┤ S ├─┤ X ├───────■──┤ H ├┤ S ├──────░─┤ Sdg ├┤ H ├─────»\n",
      "         └───┘ └───┘  └───┘ └───┘          └───┘└───┘      ░ └─────┘└───┘     »\n",
      " cr_0: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      " cr_1: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      " cr_2: 0 ═════════════════════════════════════════════════════════════════════»\n",
      "                                                                              »\n",
      "«         ┌───┐     ┌───┐ ┌───┐ ┌───┐     ┌─┐   \n",
      "«qr_0: ───┤ X ├──■──┤ H ├─┤ S ├─┤ H ├─────┤M├───\n",
      "«       ░ └─┬─┘  │  └┬─┬┘ └───┘ └───┘     └╥┘   \n",
      "«qr_1: ─░───┼────┼───┤M├───────────────────╫────\n",
      "«       ░   │  ┌─┴─┐ └╥┘ ┌─────┐┌───┐┌───┐ ║ ┌─┐\n",
      "«qr_2: ─────■──┤ X ├──╫──┤ Sdg ├┤ H ├┤ H ├─╫─┤M├\n",
      "«              └───┘  ║  └─────┘└───┘└───┘ ║ └╥┘\n",
      "«cr_0: ═══════════════╬════════════════════╩══╬═\n",
      "«                     ║                       ║ \n",
      "«cr_1: ═══════════════╬═══════════════════════╩═\n",
      "«                     ║                         \n",
      "«cr_2: ═══════════════╩═════════════════════════\n",
      "«                                               \n"
     ]
    }
   ],
   "source": [
    "#Original RB circuits\n",
    "print (rb_original_circs[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:07.602148Z",
     "start_time": "2019-12-10T21:50:07.569887Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         ┌───┐┌─────┐┌───┐        ┌───┐ ┌───┐            ░ ┌───┐             »\n",
      "qr_0: |0>┤ H ├┤ Sdg ├┤ H ├──■─────┤ X ├─┤ Z ├────────────░─┤ H ├───────■─────»\n",
      "         ├───┤└──░──┘├───┤  │   ░ └─┬─┘┌┴───┴┐┌───┐┌───┐ ░ └─░─┘┌───┐  │   ░ »\n",
      "qr_1: |0>┤ H ├───░───┤ X ├──┼───░───┼──┤ Sdg ├┤ H ├┤ Y ├─────░──┤ X ├──┼───░─»\n",
      "         ├───┤ ┌───┐ ├───┤┌─┴─┐ ░   │  └┬───┬┘├───┤└───┘ ░   ░  └───┘┌─┴─┐ ░ »\n",
      "qr_2: |0>┤ H ├─┤ H ├─┤ S ├┤ X ├─────■───┤ H ├─┤ S ├──────░───────────┤ X ├───»\n",
      "         └───┘ └───┘ └───┘└───┘         └───┘ └───┘      ░           └───┘   »\n",
      " cr_0: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      " cr_1: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      " cr_2: 0 ════════════════════════════════════════════════════════════════════»\n",
      "                                                                             »\n",
      "«       ░  ┌───┐  ┌───┐                ┌─────┐┌───┐┌───┐     ┌─┐   \n",
      "«qr_0: ─░──┤ H ├──┤ S ├─────────────■──┤ Sdg ├┤ H ├┤ H ├─────┤M├───\n",
      "«       ░  ├───┤ ┌┴───┴┐┌───┐┌───┐  │  └──░──┘├───┤└─░─┘┌───┐└╥┘┌─┐\n",
      "«qr_1: ────┤ H ├─┤ Sdg ├┤ H ├┤ Y ├──┼─────░───┤ X ├──░──┤ Z ├─╫─┤M├\n",
      "«       ░ ┌┴───┴┐└┬───┬┘└───┘└───┘┌─┴─┐┌─────┐├───┤ ┌─┐ └───┘ ║ └╥┘\n",
      "«qr_2: ─░─┤ Sdg ├─┤ H ├───────────┤ X ├┤ Sdg ├┤ H ├─┤M├───────╫──╫─\n",
      "«       ░ └─────┘ └───┘           └───┘└─────┘└───┘ └╥┘       ║  ║ \n",
      "«cr_0: ══════════════════════════════════════════════╬════════╩══╬═\n",
      "«                                                    ║           ║ \n",
      "«cr_1: ══════════════════════════════════════════════╩═══════════╬═\n",
      "«                                                                ║ \n",
      "«cr_2: ══════════════════════════════════════════════════════════╩═\n",
      "«                                                                  \n"
     ]
    }
   ],
   "source": [
    "#Interleaved RB circuits\n",
    "print (rb_interleaved_circs[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the noise model\n",
    "\n",
    "We define a noise model for the simulator. To simulate decay, we add depolarizing error probabilities to the CNOT and U gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:07.618100Z",
     "start_time": "2019-12-10T21:50:07.612033Z"
    }
   },
   "outputs": [],
   "source": [
    "noise_model = NoiseModel()\n",
    "p1Q = 0.002\n",
    "p2Q = 0.01\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p1Q, 1), 'u2')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(2*p1Q, 1), 'u3')\n",
    "noise_model.add_all_qubit_quantum_error(depolarizing_error(p2Q, 2), 'cx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can execute the original and interleaved RB sequences either using a Qiskit Aer Simulator (with some noise model) or using an IBMQ provider, and obtain a list of results, `result_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:50:54.204717Z",
     "start_time": "2019-12-10T21:50:07.623426Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating Original Circuits\n"
     ]
    }
   ],
   "source": [
    "#Original RB circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 200\n",
    "original_result_list = []\n",
    "original_qobj_list = []\n",
    "import time\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_original_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})\n",
    "    original_result_list.append(job.result())\n",
    "    original_qobj_list.append(qobj)    \n",
    "print(\"Finished Simulating Original Circuits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:01.879646Z",
     "start_time": "2019-12-10T21:50:54.210288Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling seed 0\n",
      "Simulating seed 0\n",
      "Compiling seed 1\n",
      "Simulating seed 1\n",
      "Compiling seed 2\n",
      "Simulating seed 2\n",
      "Compiling seed 3\n",
      "Simulating seed 3\n",
      "Compiling seed 4\n",
      "Simulating seed 4\n",
      "Finished Simulating Interleaved Circuits\n"
     ]
    }
   ],
   "source": [
    "#Interleaved RB circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "basis_gates = ['u1','u2','u3','cx'] # use U,CX for now\n",
    "shots = 200\n",
    "interleaved_result_list = []\n",
    "interleaved_qobj_list = []\n",
    "import time\n",
    "for rb_seed,rb_circ_seed in enumerate(rb_interleaved_circs):\n",
    "    print('Compiling seed %d'%rb_seed)\n",
    "    new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)\n",
    "    qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)\n",
    "    print('Simulating seed %d'%rb_seed)\n",
    "    job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})\n",
    "    interleaved_result_list.append(job.result())\n",
    "    interleaved_qobj_list.append(qobj)    \n",
    "print(\"Finished Simulating Interleaved Circuits\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the results\n",
    "\n",
    "We fit the results of the original RB circuits and the interleaved RB circuits into an exponentially decaying function and obtain the *Errors per Clifford* $\\alpha$ and $\\alpha_C$ of the original and interleaved sequences, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:01.933539Z",
     "start_time": "2019-12-10T21:52:01.884413Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Create the original and interleaved RB fitter\n",
    "original_rb_fit = rb.RBFitter(original_result_list, xdata, rb_opts['rb_pattern'])\n",
    "interleaved_rb_fit = rb.RBFitter(interleaved_result_list, xdata, rb_opts['rb_pattern'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the interleaved gate error fidelity\n",
    "\n",
    "From the values of $\\alpha$ and $\\alpha_C$ we obtain the gate error of the interleaved Clifford $c$, and $r_C=1-$(average gate fidelity of the interleaved Clifford $C$), is estimated by:\n",
    "$$ EPC^{est} = r_C^{est} = \\frac{(2^n-1)(1-\\alpha_C/\\alpha)}{2^n}$$\n",
    "and must lie in the range given by certain systematic error bounds: \n",
    "$$[r_C^{est}-E,r_C^{est}+E]$$ \n",
    "for each of the patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:02.060263Z",
     "start_time": "2019-12-10T21:52:01.935781Z"
    }
   },
   "outputs": [],
   "source": [
    "#Calculate the joint fitter\n",
    "joint_rb_fit = rb.InterleavedRBFitter(original_result_list, interleaved_result_list, xdata, rb_opts['rb_pattern'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:02.069412Z",
     "start_time": "2019-12-10T21:52:02.062633Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pattern: 0 - 2 qubit interleaved RB: {'alpha': 0.9809881107697814, 'alpha_err': 0.0006148545596269193, 'alpha_c': 0.9728823340768097, 'alpha_c_err': 0.002193599815771871, 'epc_est': 0.006197152088783547, 'epc_est_err': 0.001740674799430036, 'systematic_err': 0.02232068175654431, 'systematic_err_L': -0.01612352966776076, 'systematic_err_R': 0.028517833845327856}\n",
      "pattern: 1 - 1 qubit interleaved RB: {'alpha': 0.9971452295642347, 'alpha_err': 0.00039653097031025356, 'alpha_c': 0.9937812177378842, 'alpha_c_err': 0.00029894950819651353, 'epc_est': 0.0016868214010413718, 'epc_est_err': 0.0002484735812357611, 'systematic_err': 0.0016868214010413718, 'systematic_err_L': 0.0, 'systematic_err_R': 0.0033736428020827436}\n"
     ]
    }
   ],
   "source": [
    "#Print the joint fitter parameters\n",
    "for patt_ind, pattern in enumerate(rb_pattern):\n",
    "    print ('pattern:', patt_ind, '-', len(pattern), 'qubit interleaved RB:', joint_rb_fit.fit_int[patt_ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:02.501467Z",
     "start_time": "2019-12-10T21:52:02.073248Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the joint RB data\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "for i in range(2):\n",
    "    ax = plt.subplot(1, 2, i+1)\n",
    "    pattern_ind = i\n",
    "\n",
    "    # Plot the essence by calling plot_rb_data\n",
    "    joint_rb_fit.plot_rb_data(pattern_ind, ax=ax, add_label=True, show_plt=False)\n",
    "\n",
    "    # Add title and label\n",
    "    ax.set_title('%d Qubit interleaved RB'%(len(rb_opts['rb_pattern'][i])), fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:52:02.569652Z",
     "start_time": "2019-12-10T21:52:02.503515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 10 16:52:02 2019 EST</td></tr></table>"
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       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
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       "<IPython.core.display.HTML object>"
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   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
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