From bda74d3717b7243c11c46e9c573d093c473a3da3 Mon Sep 17 00:00:00 2001 From: Aditya Dhumuntarao Date: Fri, 27 Oct 2023 17:14:31 -0600 Subject: [PATCH] Wrote the IBM-Q MCB for Kolkata --- .../objects/advanced/MCB_IBMQ23.ipynb | 1775 +++++++++++++++++ 1 file changed, 1775 insertions(+) create mode 100644 jupyter_notebooks/Tutorials/objects/advanced/MCB_IBMQ23.ipynb diff --git a/jupyter_notebooks/Tutorials/objects/advanced/MCB_IBMQ23.ipynb b/jupyter_notebooks/Tutorials/objects/advanced/MCB_IBMQ23.ipynb new file mode 100644 index 000000000..9567609e0 --- /dev/null +++ b/jupyter_notebooks/Tutorials/objects/advanced/MCB_IBMQ23.ipynb @@ -0,0 +1,1775 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mirror Circuit Benchmarks on New IBM-Q Devices\n", + "\n", + "Here, I run MCB on the new IBM-Q Devices. In this jupyter notebook, there are three main goals:\n", + "\n", + "1. Design the edgelist for the new 7-, 16-, 27-, and 127-qubit devices.\n", + " a. Confirm designs with network graphs. \n", + " b. Generate ibmq_xxxx.py files for each correct graph.\n", + "2. Load IBM-Q access via `qiskit-ibm-provider` with updated syntax.\n", + "3. Excute short MCB on low qubit devices. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below are the necessary packages for graph visualization. The details on the netgraph package\n", + "can be found here:\n", + "\n", + "https://github.com/paulbrodersen/netgraph\n", + "\n", + "Netgraph will have to be installed with pip if not on your devices\n", + "\n", + "`pip install netgraph`\n", + "\n", + "I installed netgraph after `conda activate pygsti`. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "from netgraph import Graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7-Qubit Devices\n", + "\n", + "The 7-qubit devices that are both currently active (as of Oct/11/2023) and that I have access to are:\n", + "\n", + "- Nairobi\n", + "- Perth\n", + "- Lagos\n", + "\n", + "The specifications for these devices is as follows. To generate design files, simply remove the \"_7\" and delete everything below `spec_format`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Specification of IBM Q Nairobi, Perth, and Lagos \"\"\"\n", + "\n", + "qubits_7 = ['Q' + str(x) for x in range(7)]\n", + "\n", + "two_qubit_gate_7 = 'Gcnot'\n", + "\n", + "edgelist_7 = [('Q1', 'Q0'),\n", + " ('Q0', 'Q1'),\n", + " ('Q2', 'Q1'),\n", + " ('Q1', 'Q2'),\n", + " ('Q1', 'Q3'),\n", + " ('Q3', 'Q1'),\n", + " ('Q3', 'Q5'),\n", + " ('Q5', 'Q3'),\n", + " ('Q4', 'Q5'),\n", + " ('Q5', 'Q4'),\n", + " ('Q6', 'Q5'),\n", + " ('Q5', 'Q6'),\n", + " ]\n", + "\n", + "spec_format_7 = 'ibmq_v2019'\n", + "\n", + "node_positions_7 = {\n", + " 'Q0' : np.array([0.0, 0.4]),\n", + " 'Q1' : np.array([0.2, 0.4]),\n", + " 'Q2' : np.array([0.4, 0.4]),\n", + " 'Q3' : np.array([0.2, 0.2]),\n", + " 'Q4' : np.array([0.0, 0.0]),\n", + " 'Q5' : np.array([0.2, 0.0]),\n", + " 'Q6' : np.array([0.4, 0.0]),\n", + "}\n", + "\n", + "\n", + "Graph(\n", + " edgelist_7,\n", + " node_layout=node_positions_7,\n", + " node_labels=True,\n", + " node_label_fontdict=dict(fontweight='bold',size=10),\n", + " node_size=5,\n", + " )\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 16-Qubit Devices\n", + "\n", + "The 16-qubit devices that are both currently active (as of Oct/11/2023) and that I have access to are:\n", + "\n", + "- Guadalupe\n", + "\n", + "It turns out that Guadalupe is already in the pygsti/extra/devices directory. I reproduced the code and checked my code \n", + "against the existing ibmq_guadalupe.py file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Specification of IBM Q Guadalupe \"\"\"\n", + "\n", + "qubits_16= ['Q' + str(x) for x in range(16)]\n", + "\n", + "two_qubit_gate_16 = 'Gcnot'\n", + "\n", + "edgelist_16 = [\n", + " # 1st row of connections\n", + " ('Q0', 'Q1'), ('Q1', 'Q0'),\n", + " ('Q1', 'Q4'), ('Q4', 'Q1'),\n", + " ('Q4', 'Q7'), ('Q7', 'Q4'),\n", + " ('Q7', 'Q10'), ('Q10', 'Q7'),\n", + " ('Q10', 'Q12'), ('Q12', 'Q10'),\n", + " ('Q12', 'Q15'), ('Q15', 'Q12'),\n", + " # 2nd row of connections\n", + " ('Q3', 'Q5'), ('Q5', 'Q3'),\n", + " ('Q5', 'Q8'), ('Q8', 'Q5'),\n", + " ('Q8', 'Q11'), ('Q11', 'Q8'),\n", + " ('Q11', 'Q14'), ('Q14', 'Q11'),\n", + " # 1st column of connections\n", + " ('Q1', 'Q2'), ('Q2', 'Q1'),\n", + " ('Q2', 'Q3'), ('Q3', 'Q2'),\n", + " # 2nd column of connections\n", + " ('Q6', 'Q7'), ('Q7', 'Q6'),\n", + " ('Q8', 'Q9'), ('Q9', 'Q8'),\n", + " # 3rd column of connections\n", + " ('Q12', 'Q13'), ('Q13', 'Q12'),\n", + " ('Q13', 'Q14'), ('Q14', 'Q13')\n", + "]\n", + "\n", + "spec_format_16 = 'ibmq_v2019'\n", + "\n", + "node_positions_16 = {\n", + " # Fifth Layer\n", + " 'Q6' : np.array([.6, 0.8]),\n", + " # Fourth Layer\n", + " 'Q0' : np.array([0.0, 0.6]),\n", + " 'Q1' : np.array([0.2, 0.6]),\n", + " 'Q4' : np.array([0.4, 0.6]),\n", + " 'Q7' : np.array([0.6, 0.6]),\n", + " 'Q10' : np.array([0.8, 0.6]),\n", + " 'Q12' : np.array([1.0, 0.6]),\n", + " 'Q15' : np.array([1.2, 0.6]),\n", + " # Third Layer\n", + " 'Q2' : np.array([0.2, 0.4]),\n", + " 'Q13' : np.array([1.0, 0.4]),\n", + " # Second Layer\n", + " 'Q3' : np.array([0.2, 0.2]),\n", + " 'Q5' : np.array([0.4, 0.2]),\n", + " 'Q8' : np.array([0.6, 0.2]),\n", + " 'Q11' : np.array([0.8, 0.2]),\n", + " 'Q14' : np.array([1.0, 0.2]),\n", + " # First Layer\n", + " 'Q9' : np.array([0.6, 0.0]),\n", + "}\n", + "\n", + "\n", + "Graph(\n", + " edgelist_16,\n", + " node_layout=node_positions_16,\n", + " node_labels=True,\n", + " node_label_fontdict=dict(fontweight='bold',size=10),\n", + " node_size=6,\n", + " )\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 27-Qubit Devices\n", + "\n", + "The 27-qubit devices that are both currently active (as of Oct/11/2023) and that I have access to are:\n", + "\n", + "- Algiers\n", + "- Auckland\n", + "- Cairo\n", + "- Hanoi\n", + "- Kolkata\n", + "- Mumbai\n", + "\n", + "The specifications for these devices is as follows. To generate design files, simply remove the \"_27\" and delete everything below `spec_format`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/Caskroom/miniconda/base/envs/pygsti/lib/python3.10/site-packages/netgraph/_parser.py:23: UserWarning: Multi-graphs are not properly supported. Duplicate edges are plotted as a single edge; edge weights (if any) are summed.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Specification of IBM Q Algiers, Kolkata, Mumbai, Cairo, Auckland, and Hanoi \"\"\"\n", + "\n", + "qubits_27= ['Q' + str(x) for x in range(27)]\n", + "\n", + "two_qubit_gate_27 = 'Gcnot'\n", + "\n", + "edgelist_27 = [\n", + " # 1st row of connections\n", + " ('Q0', 'Q1'), ('Q1', 'Q0'),\n", + " ('Q1', 'Q4'), ('Q4', 'Q1'),\n", + " ('Q4', 'Q7'), ('Q7', 'Q4'),\n", + " ('Q7', 'Q10'), ('Q10', 'Q7'),\n", + " ('Q10', 'Q12'), ('Q12', 'Q10'),\n", + " ('Q12', 'Q15'), ('Q15', 'Q12'),\n", + " ('Q15', 'Q18'), ('Q18', 'Q15'),\n", + " ('Q18', 'Q21'), ('Q21', 'Q18'),\n", + " ('Q21', 'Q23'), ('Q21', 'Q23'),\n", + " # 2nd row of connections\n", + " ('Q3', 'Q5'), ('Q5', 'Q3'),\n", + " ('Q5', 'Q8'), ('Q8', 'Q5'),\n", + " ('Q8', 'Q11'), ('Q11', 'Q8'),\n", + " ('Q11', 'Q14'), ('Q14', 'Q11'),\n", + " ('Q14', 'Q16'), ('Q16', 'Q14'),\n", + " ('Q16', 'Q19'), ('Q19', 'Q16'),\n", + " ('Q19', 'Q22'), ('Q22', 'Q19'),\n", + " ('Q22', 'Q25'), ('Q25', 'Q22'),\n", + " ('Q25', 'Q26'), ('Q26', 'Q25'),\n", + " # 1st column of connections\n", + " ('Q1', 'Q2'), ('Q2', 'Q1'),\n", + " ('Q2', 'Q3'), ('Q3', 'Q2'),\n", + " # 2nd column of connections\n", + " ('Q6', 'Q7'), ('Q7', 'Q6'),\n", + " ('Q8', 'Q9'), ('Q9', 'Q8'),\n", + " # 3rd column of connections\n", + " ('Q12', 'Q13'), ('Q13', 'Q12'),\n", + " ('Q13', 'Q14'), ('Q14', 'Q13'),\n", + " # 4th column of connections\n", + " ('Q17', 'Q18'), ('Q18', 'Q17'),\n", + " ('Q19', 'Q20'), ('Q20', 'Q19'),\n", + " # 5th column of connections\n", + " ('Q23', 'Q24'), ('Q24', 'Q23'),\n", + " ('Q24', 'Q25'), ('Q25', 'Q24')\n", + "]\n", + "\n", + "spec_format_27 = 'ibmq_v2023'\n", + "\n", + "node_positions_27 = {\n", + " # Fifth Layer\n", + " 'Q6' : np.array([0.6, 0.8]),\n", + " 'Q17' : np.array([1.4, 0.8]),\n", + " # Fourth Layer\n", + " 'Q0' : np.array([0.0, 0.6]),\n", + " 'Q1' : np.array([0.2, 0.6]),\n", + " 'Q4' : np.array([0.4, 0.6]),\n", + " 'Q7' : np.array([0.6, 0.6]),\n", + " 'Q10' : np.array([0.8, 0.6]),\n", + " 'Q12' : np.array([1.0, 0.6]),\n", + " 'Q15' : np.array([1.2, 0.6]),\n", + " 'Q18' : np.array([1.4, 0.6]),\n", + " 'Q21' : np.array([1.6, 0.6]),\n", + " 'Q23' : np.array([1.8, 0.6]),\n", + " # Third Layer\n", + " 'Q2' : np.array([0.2, 0.4]),\n", + " 'Q13' : np.array([1.0, 0.4]),\n", + " 'Q24' : np.array([1.8, 0.4]),\n", + " # Second Layer\n", + " 'Q3' : np.array([0.2, 0.2]),\n", + " 'Q5' : np.array([0.4, 0.2]),\n", + " 'Q8' : np.array([0.6, 0.2]),\n", + " 'Q11' : np.array([0.8, 0.2]),\n", + " 'Q14' : np.array([1.0, 0.2]),\n", + " 'Q16' : np.array([1.2, 0.2]),\n", + " 'Q19' : np.array([1.4, 0.2]),\n", + " 'Q22' : np.array([1.6, 0.2]),\n", + " 'Q25' : np.array([1.8, 0.2]),\n", + " 'Q26' : np.array([2.0, 0.2]),\n", + " # First Layer\n", + " 'Q9' : np.array([0.6, 0.0]),\n", + " 'Q20' : np.array([1.4, 0.0])\n", + "}\n", + "\n", + "\n", + "Graph(\n", + " edgelist_27,\n", + " node_layout=node_positions_27,\n", + " node_labels=True,\n", + " node_label_fontdict=dict(fontweight='bold',size=10),\n", + " node_size=8,\n", + " )\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 127-Qubit Devices\n", + "\n", + "The 127-qubit devices that are both currently active (as of Oct/11/2023) and that I have access to are:\n", + "\n", + "- Brisbane\n", + "- Nazca\n", + "- Sherbrooke\n", + "\n", + "The specifications for these devices is as follows. To generate design files, simply remove the \"_127\" and delete everything below `spec_format`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\"\"\" Specification of IBM Q Brisbane, Nazca, and Sherbrooke \"\"\"\n", + "\n", + "qubits_127= ['Q' + str(x) for x in range(127)]\n", + "\n", + "two_qubit_gate_127 = 'Gcnot'\n", + "\n", + "edgelist_127 = [\n", + " # 1st row of connections\n", + " ('Q0', 'Q1'), ('Q1', 'Q0'),\n", + " ('Q1', 'Q2'), ('Q2', 'Q1'),\n", + " ('Q2', 'Q3'), ('Q3', 'Q2'),\n", + " ('Q3', 'Q4'), ('Q4', 'Q3'),\n", + " ('Q4', 'Q5'), ('Q5', 'Q4'),\n", + " ('Q5', 'Q6'), ('Q6', 'Q5'),\n", + " ('Q6', 'Q7'), ('Q7', 'Q6'),\n", + " ('Q7', 'Q8'), ('Q8', 'Q7'),\n", + " ('Q8', 'Q9'), ('Q9', 'Q8'),\n", + " ('Q9', 'Q10'), ('Q10', 'Q9'),\n", + " ('Q10', 'Q11'), ('Q11', 'Q10'),\n", + " ('Q11', 'Q12'), ('Q12', 'Q11'),\n", + " ('Q12', 'Q13'), ('Q13', 'Q12'),\n", + " # 2nd row of connections\n", + " ('Q18', 'Q19'), ('Q19', 'Q18'),\n", + " ('Q19', 'Q20'), ('Q20', 'Q19'),\n", + " ('Q20', 'Q21'), ('Q21', 'Q20'),\n", + " ('Q21', 'Q22'), ('Q22', 'Q21'),\n", + " ('Q22', 'Q23'), ('Q23', 'Q22'),\n", + " ('Q23', 'Q24'), ('Q24', 'Q23'),\n", + " ('Q24', 'Q25'), ('Q25', 'Q24'),\n", + " ('Q25', 'Q26'), ('Q26', 'Q25'),\n", + " ('Q26', 'Q27'), ('Q27', 'Q26'),\n", + " ('Q27', 'Q28'), ('Q28', 'Q27'),\n", + " ('Q28', 'Q29'), ('Q29', 'Q28'),\n", + " ('Q29', 'Q30'), ('Q30', 'Q29'),\n", + " ('Q30', 'Q31'), ('Q31', 'Q30'),\n", + " ('Q31', 'Q32'), ('Q32', 'Q31'),\n", + " # 3rd row of connections\n", + " ('Q37', 'Q38'), ('Q38', 'Q37'),\n", + " ('Q38', 'Q39'), ('Q39', 'Q38'),\n", + " ('Q39', 'Q40'), ('Q40', 'Q39'),\n", + " ('Q40', 'Q41'), ('Q41', 'Q40'),\n", + " ('Q41', 'Q42'), ('Q42', 'Q41'),\n", + " ('Q42', 'Q43'), ('Q43', 'Q42'),\n", + " ('Q43', 'Q44'), ('Q44', 'Q43'),\n", + " ('Q44', 'Q45'), ('Q45', 'Q44'),\n", + " ('Q45', 'Q46'), ('Q46', 'Q45'),\n", + " ('Q46', 'Q47'), ('Q47', 'Q46'),\n", + " ('Q47', 'Q48'), ('Q48', 'Q47'),\n", + " ('Q48', 'Q49'), ('Q49', 'Q48'),\n", + " ('Q49', 'Q50'), ('Q50', 'Q49'),\n", + " ('Q50', 'Q51'), ('Q51', 'Q50'),\n", + " # 4th row of connections\n", + " ('Q56', 'Q57'), ('Q57', 'Q56'),\n", + " ('Q57', 'Q58'), ('Q58', 'Q57'),\n", + " ('Q58', 'Q59'), ('Q59', 'Q58'),\n", + " ('Q59', 'Q60'), ('Q60', 'Q59'),\n", + " ('Q60', 'Q61'), ('Q61', 'Q60'),\n", + " ('Q61', 'Q62'), ('Q62', 'Q61'),\n", + " ('Q62', 'Q63'), ('Q63', 'Q62'),\n", + " ('Q63', 'Q64'), ('Q64', 'Q63'),\n", + " ('Q64', 'Q65'), ('Q65', 'Q64'),\n", + " ('Q65', 'Q66'), ('Q66', 'Q65'),\n", + " ('Q66', 'Q67'), ('Q67', 'Q66'),\n", + " ('Q67', 'Q68'), ('Q68', 'Q67'),\n", + " ('Q68', 'Q69'), ('Q69', 'Q68'),\n", + " ('Q69', 'Q70'), ('Q70', 'Q69'),\n", + " # 5th row of connections\n", + " ('Q75', 'Q76'), ('Q76', 'Q75'), \n", + " ('Q76', 'Q77'), ('Q77', 'Q76'),\n", + " ('Q77', 'Q78'), ('Q78', 'Q77'),\n", + " ('Q78', 'Q79'), ('Q79', 'Q78'),\n", + " ('Q79', 'Q80'), ('Q80', 'Q79'),\n", + " ('Q80', 'Q81'), ('Q81', 'Q80'),\n", + " ('Q81', 'Q82'), ('Q82', 'Q81'),\n", + " ('Q82', 'Q83'), ('Q83', 'Q82'),\n", + " ('Q83', 'Q84'), ('Q84', 'Q83'),\n", + " ('Q84', 'Q85'), ('Q85', 'Q84'),\n", + " ('Q85', 'Q86'), ('Q86', 'Q85'),\n", + " ('Q86', 'Q87'), ('Q87', 'Q86'),\n", + " ('Q87', 'Q88'), ('Q88', 'Q87'),\n", + " ('Q88', 'Q89'), ('Q89', 'Q88'),\n", + " # 6th row of connections\n", + " ('Q94', 'Q95'), ('Q95', 'Q94'),\n", + " ('Q95', 'Q96'), ('Q96', 'Q95'), \n", + " ('Q96', 'Q97'), ('Q97', 'Q96'),\n", + " ('Q97', 'Q98'), ('Q98', 'Q97'),\n", + " ('Q98', 'Q99'), ('Q99', 'Q98'),\n", + " ('Q99', 'Q100'), ('Q100', 'Q99'),\n", + " ('Q100', 'Q101'), ('Q101', 'Q100'),\n", + " ('Q101', 'Q102'), ('Q102', 'Q101'),\n", + " ('Q102', 'Q103'), ('Q103', 'Q102'),\n", + " ('Q103', 'Q104'), ('Q104', 'Q103'),\n", + " ('Q104', 'Q105'), ('Q105', 'Q104'),\n", + " ('Q105', 'Q106'), ('Q106', 'Q105'),\n", + " ('Q106', 'Q107'), ('Q107', 'Q106'),\n", + " ('Q107', 'Q108'), ('Q108', 'Q107'),\n", + " # 7th row of connections\n", + " ('Q113', 'Q114'), ('Q114', 'Q113'),\n", + " ('Q114', 'Q115'), ('Q115', 'Q114'),\n", + " ('Q115', 'Q116'), ('Q116', 'Q115'),\n", + " ('Q116', 'Q117'), ('Q117', 'Q116'),\n", + " ('Q117', 'Q118'), ('Q118', 'Q117'),\n", + " ('Q118', 'Q119'), ('Q119', 'Q118'),\n", + " ('Q119', 'Q120'), ('Q120', 'Q119'),\n", + " ('Q120', 'Q121'), ('Q121', 'Q120'),\n", + " ('Q121', 'Q122'), ('Q122', 'Q121'),\n", + " ('Q122', 'Q123'), ('Q123', 'Q122'),\n", + " ('Q123', 'Q124'), ('Q124', 'Q123'),\n", + " ('Q124', 'Q125'), ('Q125', 'Q124'),\n", + " ('Q125', 'Q126'), ('Q126', 'Q125'),\n", + " # 1st column of connections\n", + " ('Q0', 'Q14'), ('Q14', 'Q0'),\n", + " ('Q14', 'Q18'), ('Q18', 'Q14'),\n", + " ('Q37', 'Q52'), ('Q52', 'Q37'),\n", + " ('Q52', 'Q56'), ('Q56', 'Q52'),\n", + " ('Q75', 'Q90'), ('Q90', 'Q75'),\n", + " ('Q90', 'Q94'), ('Q94', 'Q90'),\n", + " # 2nd column of connections\n", + " ('Q20', 'Q33'), ('Q33', 'Q20'),\n", + " ('Q33', 'Q39'), ('Q39', 'Q33'),\n", + " ('Q58', 'Q71'), ('Q71', 'Q58'),\n", + " ('Q71', 'Q77'), ('Q77', 'Q71'),\n", + " ('Q96', 'Q109'), ('Q109', 'Q96'),\n", + " ('Q109', 'Q114'), ('Q114', 'Q109'),\n", + " # 3rd column of connections\n", + " ('Q4', 'Q15'), ('Q15', 'Q4'),\n", + " ('Q15', 'Q22'), ('Q22', 'Q15'),\n", + " ('Q41', 'Q53'), ('Q53', 'Q41'),\n", + " ('Q53', 'Q60'), ('Q60', 'Q53'),\n", + " ('Q79', 'Q91'), ('Q91', 'Q79'),\n", + " ('Q91', 'Q98'), ('Q98', 'Q91'),\n", + " # 4th column of connections\n", + " ('Q24', 'Q34'), ('Q34', 'Q24'),\n", + " ('Q34', 'Q43'), ('Q43', 'Q34'),\n", + " ('Q62', 'Q72'), ('Q72', 'Q62'),\n", + " ('Q72', 'Q81'), ('Q81', 'Q72'),\n", + " ('Q100', 'Q110'), ('Q110', 'Q100'),\n", + " ('Q110', 'Q118'), ('Q118', 'Q110'),\n", + " # 5th column of connections\n", + " ('Q8', 'Q16'), ('Q16', 'Q8'),\n", + " ('Q16', 'Q26'), ('Q26', 'Q16'),\n", + " ('Q45', 'Q54'), ('Q54', 'Q45'),\n", + " ('Q54', 'Q64'), ('Q64', 'Q54'),\n", + " ('Q83', 'Q92'), ('Q92', 'Q83'),\n", + " ('Q92', 'Q102'), ('Q102', 'Q92'),\n", + " # 6th column of connections\n", + " ('Q28', 'Q35'), ('Q35', 'Q28'),\n", + " ('Q35', 'Q47'), ('Q47', 'Q35'),\n", + " ('Q66', 'Q73'), ('Q73', 'Q66'),\n", + " ('Q73', 'Q85'), ('Q85', 'Q73'),\n", + " ('Q104', 'Q111'), ('Q111', 'Q104'),\n", + " ('Q111', 'Q122'), ('Q122', 'Q111'),\n", + " # 7th column of connections\n", + " ('Q12', 'Q17'), ('Q17', 'Q12'),\n", + " ('Q17', 'Q30'), ('Q30', 'Q17'),\n", + " ('Q49', 'Q55'), ('Q55', 'Q49'),\n", + " ('Q55', 'Q68'), ('Q68', 'Q55'),\n", + " ('Q87', 'Q93'), ('Q93', 'Q87'),\n", + " ('Q93', 'Q106'), ('Q106', 'Q93'),\n", + " # 8th column of connections\n", + " ('Q32', 'Q36'), ('Q36', 'Q32'),\n", + " ('Q36', 'Q51'), ('Q51', 'Q36'),\n", + " ('Q70', 'Q74'), ('Q74', 'Q70'),\n", + " ('Q74', 'Q89'), ('Q89', 'Q74'),\n", + " ('Q108', 'Q112'), ('Q112', 'Q108'),\n", + " ('Q112', 'Q126'), ('Q126', 'Q112'),\n", + "]\n", + "\n", + "spec_format_127 = 'ibmq_v2019'\n", + "\n", + "node_positions_127 = {\n", + " # 1st row of connections\n", + " 'Q0' : np.array([0.0,2.4]),\n", + " 'Q1' : np.array([0.2,2.4]),\n", + " 'Q2' : np.array([0.4,2.4]),\n", + " 'Q3' : np.array([0.6,2.4]),\n", + " 'Q4' : np.array([0.8,2.4]),\n", + " 'Q5' : np.array([1.0,2.4]),\n", + " 'Q6' : np.array([1.2,2.4]),\n", + " 'Q7' : np.array([1.4,2.4]),\n", + " 'Q8' : np.array([1.6,2.4]),\n", + " 'Q9' : np.array([1.8,2.4]),\n", + " 'Q10' : np.array([2.0,2.4]),\n", + " 'Q11' : np.array([2.2,2.4]),\n", + " 'Q12' : np.array([2.4,2.4]),\n", + " 'Q13' : np.array([2.6,2.4]),\n", + " # 2nd row of connections\n", + " 'Q18' : np.array([0.0,2.0]),\n", + " 'Q19' : np.array([0.2,2.0]),\n", + " 'Q20' : np.array([0.4,2.0]),\n", + " 'Q21' : np.array([0.6,2.0]),\n", + " 'Q22' : np.array([0.8,2.0]),\n", + " 'Q23' : np.array([1.0,2.0]),\n", + " 'Q24' : np.array([1.2,2.0]),\n", + " 'Q25' : np.array([1.4,2.0]),\n", + " 'Q26' : np.array([1.6,2.0]),\n", + " 'Q27' : np.array([1.8,2.0]),\n", + " 'Q28' : np.array([2.0,2.0]),\n", + " 'Q29' : np.array([2.2,2.0]),\n", + " 'Q30' : np.array([2.4,2.0]),\n", + " 'Q31' : np.array([2.6,2.0]),\n", + " # 3rd row of connections\n", + " 'Q37' : np.array([0.0,1.6]),\n", + " 'Q38' : np.array([0.2,1.6]),\n", + " 'Q39' : np.array([0.4,1.6]),\n", + " 'Q40' : np.array([0.6,1.6]),\n", + " 'Q41' : np.array([0.8,1.6]),\n", + " 'Q42' : np.array([1.0,1.6]),\n", + " 'Q43' : np.array([1.2,1.6]),\n", + " 'Q44' : np.array([1.4,1.6]),\n", + " 'Q45' : np.array([1.6,1.6]),\n", + " 'Q46' : np.array([1.8,1.6]),\n", + " 'Q47' : np.array([2.0,1.6]),\n", + " 'Q48' : np.array([2.2,1.6]),\n", + " 'Q49' : np.array([2.4,1.6]),\n", + " 'Q50' : np.array([2.6,1.6]),\n", + " # 4th row of connections\n", + " 'Q56' : np.array([0.0,1.2]),\n", + " 'Q57' : np.array([0.2,1.2]),\n", + " 'Q58' : np.array([0.4,1.2]),\n", + " 'Q59' : np.array([0.6,1.2]),\n", + " 'Q60' : np.array([0.8,1.2]),\n", + " 'Q61' : np.array([1.0,1.2]),\n", + " 'Q62' : np.array([1.2,1.2]),\n", + " 'Q63' : np.array([1.4,1.2]),\n", + " 'Q64' : np.array([1.6,1.2]),\n", + " 'Q65' : np.array([1.8,1.2]),\n", + " 'Q66' : np.array([2.0,1.2]),\n", + " 'Q67' : np.array([2.2,1.2]),\n", + " 'Q68' : np.array([2.4,1.2]),\n", + " 'Q69' : np.array([2.6,1.2]),\n", + " # 5th row of connections\n", + " 'Q75' : np.array([0.0,0.8]),\n", + " 'Q76' : np.array([0.2,0.8]),\n", + " 'Q77' : np.array([0.4,0.8]),\n", + " 'Q78' : np.array([0.6,0.8]),\n", + " 'Q79' : np.array([0.8,0.8]),\n", + " 'Q80' : np.array([1.0,0.8]),\n", + " 'Q81' : np.array([1.2,0.8]),\n", + " 'Q82' : np.array([1.4,0.8]),\n", + " 'Q83' : np.array([1.6,0.8]),\n", + " 'Q84' : np.array([1.8,0.8]),\n", + " 'Q85' : np.array([2.0,0.8]),\n", + " 'Q86' : np.array([2.2,0.8]),\n", + " 'Q87' : np.array([2.4,0.8]),\n", + " 'Q88' : np.array([2.6,0.8]),\n", + " # 6th row of connections\n", + " 'Q94' : np.array([0.0,0.4]),\n", + " 'Q95' : np.array([0.2,0.4]),\n", + " 'Q96' : np.array([0.4,0.4]),\n", + " 'Q97' : np.array([0.6,0.4]),\n", + " 'Q98' : np.array([0.8,0.4]),\n", + " 'Q99' : np.array([1.0,0.4]),\n", + " 'Q100' : np.array([1.2,0.4]),\n", + " 'Q101' : np.array([1.4,0.4]),\n", + " 'Q102' : np.array([1.6,0.4]),\n", + " 'Q103' : np.array([1.8,0.4]),\n", + " 'Q104' : np.array([2.0,0.4]),\n", + " 'Q105' : np.array([2.2,0.4]),\n", + " 'Q106' : np.array([2.4,0.4]),\n", + " 'Q107' : np.array([2.6,0.4]),\n", + " # 7th row of connections\n", + " 'Q113' : np.array([0.2,0.0]),\n", + " 'Q114' : np.array([0.4,0.0]),\n", + " 'Q115' : np.array([0.6,0.0]),\n", + " 'Q116' : np.array([0.8,0.0]),\n", + " 'Q117' : np.array([1.0,0.0]),\n", + " 'Q118' : np.array([1.2,0.0]),\n", + " 'Q119' : np.array([1.4,0.0]),\n", + " 'Q120' : np.array([1.6,0.0]),\n", + " 'Q121' : np.array([1.8,0.0]),\n", + " 'Q122' : np.array([2.0,0.0]),\n", + " 'Q123' : np.array([2.2,0.0]),\n", + " 'Q124' : np.array([2.4,0.0]),\n", + " 'Q125' : np.array([2.6,0.0]),\n", + " # 1st column of connections\n", + " 'Q14' : np.array([0.0,2.2]),\n", + " 'Q52' : np.array([0.0,1.4]),\n", + " 'Q90' : np.array([0.0,0.6]),\n", + " # 2nd column of connections\n", + " 'Q33' : np.array([0.4,1.8]),\n", + " 'Q71' : np.array([0.4,1.0]),\n", + " 'Q109' : np.array([0.4,0.2]),\n", + " # 3rd column of connections\n", + " 'Q15' : np.array([0.8,2.2]),\n", + " 'Q53' : np.array([0.8,1.4]),\n", + " 'Q91' : np.array([0.8,0.6]),\n", + " # 4th column of connections\n", + " 'Q34' : np.array([1.2,1.8]),\n", + " 'Q72' : np.array([1.2,1.0]),\n", + " 'Q110' : np.array([1.2,0.2]),\n", + " # 5th column of connections\n", + " 'Q16' : np.array([1.6,2.2]),\n", + " 'Q54' : np.array([1.6,1.4]),\n", + " 'Q92' : np.array([1.6,0.6]),\n", + " # 6th column of connections\n", + " 'Q35' : np.array([2.0,1.8]),\n", + " 'Q73' : np.array([2.0,1.0]),\n", + " 'Q111' : np.array([2.0,0.2]),\n", + " # 7th column of connections\n", + " 'Q17': np.array([2.4,2.2]),\n", + " 'Q55': np.array([2.4,1.4]),\n", + " 'Q93': np.array([2.4,0.6]),\n", + " # 8th column of connections\n", + " 'Q32' : np.array([2.8,2.0]),\n", + " 'Q36' : np.array([2.8,1.8]),\n", + " 'Q51' : np.array([2.8,1.6]),\n", + " 'Q70' : np.array([2.8,1.2]),\n", + " 'Q74' : np.array([2.8,1.0]),\n", + " 'Q89' : np.array([2.8,0.8]),\n", + " 'Q108' : np.array([2.8,0.4]),\n", + " 'Q112' : np.array([2.8,0.2]),\n", + " 'Q126' : np.array([2.8,0.0]),\n", + "}\n", + "\n", + "\n", + "Graph(\n", + " edgelist_127,\n", + " node_layout=node_positions_127,\n", + " node_labels=True,\n", + " node_label_fontdict=dict(fontweight='bold',size=5),\n", + " node_size=8,\n", + " edge_width=2\n", + " )\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IBM-Q " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As of October 11 2023, the objects/advanced/IBMQExperiments.ipynb tutorial is deprecated and will not load IBM-Q access from the `provider`. Here, we describe how to load IBM-Q access to start running MCB circuits." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import qiskit\n", + "from qiskit_ibm_provider import IBMProvider\n", + "\n", + "# Load saved account credentials.\n", + "provider = IBMProvider()\n", + "\n", + "# If these account credentials are unknown, then\n", + "# log into IBM-Q, take the API token, and set it here\n", + "# IBMProvider.save_account(token='your IBM-Q token here')\n", + "\n", + "# See which devices you have access to\n", + "\n", + "for p in provider.backends():\n", + " print(p)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Provided the above code snippet works, then we can proceed to excuting a short MCB." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MCBs on New Hardware" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's collect all the new devices (not simulators). These are collected by qubits, and by alphabetical order in each qubit size category." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "new_devices = [\n", + " ('ibm_lagos',7),\n", + " ('ibm_nairobi',7),\n", + " ('ibm_perth',7),\n", + " ('ibmq_guadalupe',16),\n", + " ('ibm_auckland',27),\n", + " ('ibm_cairo',27),\n", + " ('ibmq_mumbai',27),\n", + " ('ibm_algiers',27),\n", + " ('ibm_hanoi',27),\n", + " ('ibmq_kolkata',27),\n", + " ('ibm_nazca',127),\n", + " ('ibm_brisbane',127),\n", + " ('ibm_sherbrooke',127),\n", + "]\n", + "\n", + "lnd, wnd = np.shape(new_devices)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "new_backends = [0]*lnd\n", + "device_names = [0]*lnd\n", + "num_qubit = [0]*lnd\n", + "for i in range(lnd):\n", + " new_backends[i] = provider.get_backend(new_devices[i][0])\n", + " device_names[i], num_qubit[i] = new_devices[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we create can a processor spec. For testing purposes, let's consider IBM-Q Lagos." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import pygsti\n", + "from pygsti.extras import devices\n", + "from pygsti.extras import ibmq\n", + "from pygsti.processors import QubitProcessorSpec, CliffordCompilationRules\n", + "from pygsti.protocols import MirrorRBDesign as RMCDesign\n", + "from pygsti.protocols import PeriodicMirrorCircuitDesign as PMCDesign\n", + "from pygsti.protocols import ByDepthSummaryStatistics as SummaryStats\n", + "\n", + "# Lagos Run, num_qubit[0] = # Qubits in Lagos\n", + "\n", + "n_qubits = num_qubit[3]\n", + "qubit_labels = ['Q'+str(i) for i in range(n_qubits)]\n", + "gate_names = ['Gc{}'.format(i) for i in range(24)]\n", + "pspec_guadalupe = devices.create_processor_spec(device_names[3], gate_names)\n", + " # construct_models=('clifford',) ) \n", + "\n", + "# I believe construct_models is deprecated on pyGSTi 0.9." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "clifford_compilations_guadalupe = {'absolute': pygsti.processors.CliffordCompilationRules.create_standard(pspec_guadalupe, verbosity=0)}" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# This cell is utility code for deciding how long the longest circuits should be. You\n", + "# may want to choose this by hand instead.\n", + "\n", + "# A guess at the rough per-qubit error rate for picking the maximum depth\n", + "# to go to at each width. Change as appropriate. Putting this too low will\n", + "# just mean you run longer and more circuits than necessary.\n", + "estimated_qubit_error_rate = 0.005\n", + "\n", + "# Heuristic for removing depths that are so long that you'll get no\n", + "# useful data for w-qubit circuits. You could do this another way.\n", + "def trim_depths(depths, w):\n", + " target_polarization = 0.01 \n", + " maxdepth = np.log(target_polarization)/(w * np.log(1 - estimated_qubit_error_rate))\n", + " trimmed_depths = [d for d in depths if d < maxdepth]\n", + " numdepths = len(trimmed_depths)\n", + " if numdepths < len(depths) and trimmed_depths[-1] < maxdepth:\n", + " trimmed_depths.append(depths[numdepths])\n", + " return trimmed_depths" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 [0, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] [('Q0',)]\n", + "2 [0, 2, 4, 8, 16, 32, 64, 128, 256, 512] [('Q0', 'Q1')]\n", + "3 [0, 2, 4, 8, 16, 32, 64, 128, 256, 512] [('Q0', 'Q1', 'Q2')]\n", + "4 [0, 2, 4, 8, 16, 32, 64, 128, 256] [('Q0', 'Q1', 'Q2', 'Q3')]\n", + "5 [0, 2, 4, 8, 16, 32, 64, 128, 256] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4')]\n", + "6 [0, 2, 4, 8, 16, 32, 64, 128, 256] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5')]\n", + "7 [0, 2, 4, 8, 16, 32, 64, 128, 256] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6')]\n", + "8 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7')]\n", + "9 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8')]\n", + "10 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9')]\n", + "11 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10')]\n", + "12 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11')]\n", + "13 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12')]\n", + "14 [0, 2, 4, 8, 16, 32, 64, 128] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13')]\n", + "15 [0, 2, 4, 8, 16, 32, 64] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14')]\n", + "16 [0, 2, 4, 8, 16, 32, 64] [('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14', 'Q15')]\n" + ] + } + ], + "source": [ + "# This cell sets the circuit sampling parameters. These parameters are chosen\n", + "# so as to replicate the experiments shown in Figs. 2 and 3 of arXiv:2008.11294.\n", + "\n", + "# The number of circuits per circuit shape (width and depth). Set this to 40\n", + "# to replicate the experiments in arXiv:2008.11294. A smaller number might\n", + "# be a good idea for a fast test run.\n", + "circuits_per_shape = 10\n", + "\n", + "# The circuit widths to include in the benchmark.\n", + "widths = [i for i in range(1, n_qubits + 1)]\n", + "\n", + "# The circuit depths to include, as a function of width.\n", + "base_depths = [0,] + [int(d) for d in 2**np.arange(1, 15)] # here is where the depths are set.\n", + "depths = {w:trim_depths(base_depths,w) for w in widths}\n", + "\n", + "# The one-or-more qubit subsets to test for each width. You might want\n", + "# to choose them more intelligently than here (in arXiv:2008.11294 we used the\n", + "# qubits that were \"best\" according to their RB calibration data, although\n", + "# there's nothing special about that strategy).\n", + "qubit_lists = {w:[tuple([q for q in qubit_labels[:w]])] for w in widths} # here is where the length is chosen. IBM-Q calibration data is changed.\n", + "\n", + "for w in widths:\n", + " print(w, depths[w], qubit_lists[w])\n", + " \n", + "# Sets the two-qubit gate density in the circuits (where \"density\" refers\n", + "# to the number of circuit locations occupied by a CNOT, with each CNOT\n", + "# occupying two locations).\n", + "xi = 1/8\n", + "\n", + "# this outputs the sampling paramerets" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 ('Q0',)\n", + "- Sampling 10 circuits at MRB length 0 (1 of 11 depths) with seed 695750\n", + "- Sampling 10 circuits at MRB length 2 (2 of 11 depths) with seed 695760\n", + "- Sampling 10 circuits at MRB length 4 (3 of 11 depths) with seed 695770\n", + "- Sampling 10 circuits at MRB length 8 (4 of 11 depths) with seed 695780\n", + "- Sampling 10 circuits at MRB length 16 (5 of 11 depths) with seed 695790\n", + "- Sampling 10 circuits at MRB length 32 (6 of 11 depths) with seed 695800\n", + "- Sampling 10 circuits at MRB length 64 (7 of 11 depths) with seed 695810\n", + "- Sampling 10 circuits at MRB length 128 (8 of 11 depths) with seed 695820\n", + "- Sampling 10 circuits at MRB length 256 (9 of 11 depths) with seed 695830\n", + "- Sampling 10 circuits at MRB length 512 (10 of 11 depths) with seed 695840\n", + "- Sampling 10 circuits at MRB length 1024 (11 of 11 depths) with seed 695850\n", + "2 ('Q0', 'Q1')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 10 depths) with seed 36099\n", + "- Sampling 10 circuits at MRB length 2 (2 of 10 depths) with seed 36109\n", + "- Sampling 10 circuits at MRB length 4 (3 of 10 depths) with seed 36119\n", + "- Sampling 10 circuits at MRB length 8 (4 of 10 depths) with seed 36129\n", + "- Sampling 10 circuits at MRB length 16 (5 of 10 depths) with seed 36139\n", + "- Sampling 10 circuits at MRB length 32 (6 of 10 depths) with seed 36149\n", + "- Sampling 10 circuits at MRB length 64 (7 of 10 depths) with seed 36159\n", + "- Sampling 10 circuits at MRB length 128 (8 of 10 depths) with seed 36169\n", + "- Sampling 10 circuits at MRB length 256 (9 of 10 depths) with seed 36179\n", + "- Sampling 10 circuits at MRB length 512 (10 of 10 depths) with seed 36189\n", + "3 ('Q0', 'Q1', 'Q2')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 10 depths) with seed 477856\n", + "- Sampling 10 circuits at MRB length 2 (2 of 10 depths) with seed 477866\n", + "- Sampling 10 circuits at MRB length 4 (3 of 10 depths) with seed 477876\n", + "- Sampling 10 circuits at MRB length 8 (4 of 10 depths) with seed 477886\n", + "- Sampling 10 circuits at MRB length 16 (5 of 10 depths) with seed 477896\n", + "- Sampling 10 circuits at MRB length 32 (6 of 10 depths) with seed 477906\n", + "- Sampling 10 circuits at MRB length 64 (7 of 10 depths) with seed 477916\n", + "- Sampling 10 circuits at MRB length 128 (8 of 10 depths) with seed 477926\n", + "- Sampling 10 circuits at MRB length 256 (9 of 10 depths) with seed 477936\n", + "- Sampling 10 circuits at MRB length 512 (10 of 10 depths) with seed 477946\n", + "4 ('Q0', 'Q1', 'Q2', 'Q3')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 9 depths) with seed 18911\n", + "- Sampling 10 circuits at MRB length 2 (2 of 9 depths) with seed 18921\n", + "- Sampling 10 circuits at MRB length 4 (3 of 9 depths) with seed 18931\n", + "- Sampling 10 circuits at MRB length 8 (4 of 9 depths) with seed 18941\n", + "- Sampling 10 circuits at MRB length 16 (5 of 9 depths) with seed 18951\n", + "- Sampling 10 circuits at MRB length 32 (6 of 9 depths) with seed 18961\n", + "- Sampling 10 circuits at MRB length 64 (7 of 9 depths) with seed 18971\n", + "- Sampling 10 circuits at MRB length 128 (8 of 9 depths) with seed 18981\n", + "- Sampling 10 circuits at MRB length 256 (9 of 9 depths) with seed 18991\n", + "5 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 9 depths) with seed 413859\n", + "- Sampling 10 circuits at MRB length 2 (2 of 9 depths) with seed 413869\n", + "- Sampling 10 circuits at MRB length 4 (3 of 9 depths) with seed 413879\n", + "- Sampling 10 circuits at MRB length 8 (4 of 9 depths) with seed 413889\n", + "- Sampling 10 circuits at MRB length 16 (5 of 9 depths) with seed 413899\n", + "- Sampling 10 circuits at MRB length 32 (6 of 9 depths) with seed 413909\n", + "- Sampling 10 circuits at MRB length 64 (7 of 9 depths) with seed 413919\n", + "- Sampling 10 circuits at MRB length 128 (8 of 9 depths) with seed 413929\n", + "- Sampling 10 circuits at MRB length 256 (9 of 9 depths) with seed 413939\n", + "6 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 9 depths) with seed 16041\n", + "- Sampling 10 circuits at MRB length 2 (2 of 9 depths) with seed 16051\n", + "- Sampling 10 circuits at MRB length 4 (3 of 9 depths) with seed 16061\n", + "- Sampling 10 circuits at MRB length 8 (4 of 9 depths) with seed 16071\n", + "- Sampling 10 circuits at MRB length 16 (5 of 9 depths) with seed 16081\n", + "- Sampling 10 circuits at MRB length 32 (6 of 9 depths) with seed 16091\n", + "- Sampling 10 circuits at MRB length 64 (7 of 9 depths) with seed 16101\n", + "- Sampling 10 circuits at MRB length 128 (8 of 9 depths) with seed 16111\n", + "- Sampling 10 circuits at MRB length 256 (9 of 9 depths) with seed 16121\n", + "7 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 9 depths) with seed 215803\n", + "- Sampling 10 circuits at MRB length 2 (2 of 9 depths) with seed 215813\n", + "- Sampling 10 circuits at MRB length 4 (3 of 9 depths) with seed 215823\n", + "- Sampling 10 circuits at MRB length 8 (4 of 9 depths) with seed 215833\n", + "- Sampling 10 circuits at MRB length 16 (5 of 9 depths) with seed 215843\n", + "- Sampling 10 circuits at MRB length 32 (6 of 9 depths) with seed 215853\n", + "- Sampling 10 circuits at MRB length 64 (7 of 9 depths) with seed 215863\n", + "- Sampling 10 circuits at MRB length 128 (8 of 9 depths) with seed 215873\n", + "- Sampling 10 circuits at MRB length 256 (9 of 9 depths) with seed 215883\n", + "8 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 85301\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 85311\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 85321\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 85331\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 85341\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 85351\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 85361\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 85371\n", + "9 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 689459\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 689469\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 689479\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 689489\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 689499\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 689509\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 689519\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 689529\n", + "10 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 890253\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 890263\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 890273\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 890283\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 890293\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 890303\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 890313\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 890323\n", + "11 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 449986\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 449996\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 450006\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 450016\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 450026\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 450036\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 450046\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 450056\n", + "12 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 355373\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 355383\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 355393\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 355403\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 355413\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 355423\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 355433\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 355443\n", + "13 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 372181\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 372191\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 372201\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 372211\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 372221\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 372231\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 372241\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 372251\n", + "14 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 8 depths) with seed 482344\n", + "- Sampling 10 circuits at MRB length 2 (2 of 8 depths) with seed 482354\n", + "- Sampling 10 circuits at MRB length 4 (3 of 8 depths) with seed 482364\n", + "- Sampling 10 circuits at MRB length 8 (4 of 8 depths) with seed 482374\n", + "- Sampling 10 circuits at MRB length 16 (5 of 8 depths) with seed 482384\n", + "- Sampling 10 circuits at MRB length 32 (6 of 8 depths) with seed 482394\n", + "- Sampling 10 circuits at MRB length 64 (7 of 8 depths) with seed 482404\n", + "- Sampling 10 circuits at MRB length 128 (8 of 8 depths) with seed 482414\n", + "15 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 7 depths) with seed 594558\n", + "- Sampling 10 circuits at MRB length 2 (2 of 7 depths) with seed 594568\n", + "- Sampling 10 circuits at MRB length 4 (3 of 7 depths) with seed 594578\n", + "- Sampling 10 circuits at MRB length 8 (4 of 7 depths) with seed 594588\n", + "- Sampling 10 circuits at MRB length 16 (5 of 7 depths) with seed 594598\n", + "- Sampling 10 circuits at MRB length 32 (6 of 7 depths) with seed 594608\n", + "- Sampling 10 circuits at MRB length 64 (7 of 7 depths) with seed 594618\n", + "16 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14', 'Q15')\n", + "- Sampling 10 circuits at MRB length 0 (1 of 7 depths) with seed 329517\n", + "- Sampling 10 circuits at MRB length 2 (2 of 7 depths) with seed 329527\n", + "- Sampling 10 circuits at MRB length 4 (3 of 7 depths) with seed 329537\n", + "- Sampling 10 circuits at MRB length 8 (4 of 7 depths) with seed 329547\n", + "- Sampling 10 circuits at MRB length 16 (5 of 7 depths) with seed 329557\n", + "- Sampling 10 circuits at MRB length 32 (6 of 7 depths) with seed 329567\n", + "- Sampling 10 circuits at MRB length 64 (7 of 7 depths) with seed 329577\n" + ] + } + ], + "source": [ + "# Samples randomized mirror circuits, using the `edgegrab` sampler with the two-qubit gate\n", + "# density specified above. The `edgegrab` sampler is what is used in the experiments of\n", + "# Figs. 2 and 3 in arXiv:2008.11294 (and not what was used for the experiments of Fig. 1d).\n", + "edesigns = {} # here is the place where your experiment design is made. I will need to focus on this.\n", + "for w in widths:\n", + " for qs in qubit_lists[w]:\n", + " print(w, qs)\n", + " key = str(w) + '-' + '-'.join(qs) + '-' + 'RMCs'\n", + " edesigns[key] = RMCDesign(pspec_guadalupe, depths[w], circuits_per_shape, # future project > make this better, better kinds.\n", + " clifford_compilations=clifford_compilations_guadalupe,\n", + " qubit_labels=qs, sampler='edgegrab', \n", + " samplerargs=[2 * xi,]) # the pspec respects the configuration." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 ('Q0',)\n", + "2 ('Q0', 'Q1')\n", + "3 ('Q0', 'Q1', 'Q2')\n", + "4 ('Q0', 'Q1', 'Q2', 'Q3')\n", + "5 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4')\n", + "6 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5')\n", + "7 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6')\n", + "8 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7')\n", + "9 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8')\n", + "10 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9')\n", + "11 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10')\n", + "12 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11')\n", + "13 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12')\n", + "14 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13')\n", + "15 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14')\n", + "16 ('Q0', 'Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11', 'Q12', 'Q13', 'Q14', 'Q15')\n" + ] + } + ], + "source": [ + "# Samples periodic mirror circuits using the random germ selection algorithm specified\n", + "# in arXiv:2008.11294, designed to match the RMCs sampled above (except that they're\n", + "# periodic, not disordered).\n", + "for w in widths:\n", + " for qs in qubit_lists[w]:\n", + " print(w, qs)\n", + " key = str(w) + '-' + '-'.join(qs) + '-' + 'PMCs'\n", + " edesigns[key] = PMCDesign(pspec_guadalupe, depths[w], circuits_per_shape, # This is benchmark 2, details in the paper.\n", + " clifford_compilations=clifford_compilations_guadalupe, \n", + " qubit_labels=qs, sampler='edgegrab', \n", + " samplerargs=[xi,])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "edesign = pygsti.protocols.CombinedExperimentDesign(edesigns) # Dressed up dictionary.\n", + "\n", + "pygsti.io.write_empty_protocol_data('../../tutorial_files/test_mirror_benchmark_guadalupe', edesign) # exisitng directories are preserved when clobber is gone\n", + "\n", + "# All the circuits that need to be run\n", + "circuits = edesign.all_circuits_needing_data # usually a few thousand circuits.\n", + "\n", + "# Shuffle the circuits: best to run them in a random order.\n", + "np.random.shuffle(circuits) \n", + "\n", + "# Write this circuit list to file (the non-random order list is in the edesign folder).\n", + "pygsti.io.write_circuit_list('../../tutorial_files/test_mirror_benchmark_guadalupe/randomized_circuits_guadalupe.txt', circuits)\n", + "\n", + "# Convert to a list of OpenQASM format circuits. You may or may not want to use this \n", + "# to get the pyGSTi circuits into a format that you can run on your device.\n", + "# qasm = [c.convert_to_openqasm(standard_gates_version='x-sx-rz') for c in circuits]\n", + "\n", + "# You'd then run the circuits of `qasm` or `circuits` and put them into a pyGSTi dataset\n", + "# that replaces the empty dataset `test_mirror_benchmark_lagos/data/dataset.txt`. Below we instead\n", + "# create simulated data." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constructing job for circuit batch 1 of 37\n", + "Constructing job for circuit batch 2 of 37\n", + "Constructing job for circuit batch 3 of 37\n", + "Constructing job for circuit batch 4 of 37\n", + "Constructing job for circuit batch 5 of 37\n", + "Constructing job for circuit batch 6 of 37\n", + "Constructing job for circuit batch 7 of 37\n", + "Constructing job for circuit batch 8 of 37\n", + "Constructing job for circuit batch 9 of 37\n", + "Constructing job for circuit batch 10 of 37\n", + "Constructing job for circuit batch 11 of 37\n", + "Constructing job for circuit batch 12 of 37\n", + "Constructing job for circuit batch 13 of 37\n", + "Constructing job for circuit batch 14 of 37\n", + "Constructing job for circuit batch 15 of 37\n", + "Constructing job for circuit batch 16 of 37\n", + "Constructing job for circuit batch 17 of 37\n", + "Constructing job for circuit batch 18 of 37\n", + "Constructing job for circuit batch 19 of 37\n", + "Constructing job for circuit batch 20 of 37\n", + "Constructing job for circuit batch 21 of 37\n", + "Constructing job for circuit batch 22 of 37\n", + "Constructing job for circuit batch 23 of 37\n", + "Constructing job for circuit batch 24 of 37\n", + "Constructing job for circuit batch 25 of 37\n", + "Constructing job for circuit batch 26 of 37\n", + "Constructing job for circuit batch 27 of 37\n", + "Constructing job for circuit batch 28 of 37\n", + "Constructing job for circuit batch 29 of 37\n", + "Constructing job for circuit batch 30 of 37\n", + "Constructing job for circuit batch 31 of 37\n", + "Constructing job for circuit batch 32 of 37\n", + "Constructing job for circuit batch 33 of 37\n", + "Constructing job for circuit batch 34 of 37\n", + "Constructing job for circuit batch 35 of 37\n", + "Constructing job for circuit batch 36 of 37\n", + "Constructing job for circuit batch 37 of 37\n" + ] + } + ], + "source": [ + "# load_exp = ibmq.IBMQExperiment.from_dir('../../tutorial_files/test_mirror_benchmark_lagos')\n", + "\n", + "exp_guadalupe = ibmq.IBMQExperiment(edesign, pspec_guadalupe, circuits_per_batch=75, num_shots=1024)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Submitting batch 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_p/xf5xnlx96zn2j4rhy_02hlfm0048tm/T/ipykernel_24318/1628044699.py:1: DeprecationWarning: Support for the 'id' instruction has been deprecated from IBM hardware backends. Any 'id' instructions will be replaced with their equivalent 'delay' instruction. Please use the 'delay' instruction instead.\n", + " exp_guadalupe.submit(new_backends[3])\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Job ID is cmq51gegh50g008jm01g\n", + " - Queue position is None\n", + "Submitting batch 2\n", + " - Job ID is cmq51heb9x9g0088tg30\n", + " - Queue position is None\n", + "Submitting batch 3\n", + " - Job ID is cmq51k6yj8fg0080nbfg\n", + " - Queue position is None\n", + "Submitting batch 4\n", + " - Job ID is cmq51mpyj8fg0080nbg0\n", + " - Queue position is None\n", + "Submitting batch 5\n", + " - Job ID is cmq51nyz31fg008xzqag\n", + " - Queue position is None\n", + "Submitting batch 6\n", + " - Job ID is cmq51q6b9x9g0088tg40\n", + " - Queue position is None\n", + "Submitting batch 7\n", + " - Job ID is cmq51s7cbtrg008825j0\n", + " - Queue position is None\n", + "Submitting batch 8\n", + " - Job ID is cmq51t7gh50g008jm030\n", + " - Queue position is None\n", + "Submitting batch 9\n", + " - Job ID is cmq51v7z31fg008xzqcg\n", + " - Queue position is None\n", + "Submitting batch 10\n", + " - Job ID is cmq51wfgh50g008jm03g\n", + " - Queue position is None\n", + "Submitting batch 11\n", + " - Job ID is cmq51xqyj8fg0080nbhg\n", + " - Queue position is None\n", + "Submitting batch 12\n", + " - Job ID is cmq51zfcbtrg008825kg\n", + " - Queue position is None\n", + "Submitting batch 13\n", + " - Job ID is cmq520gcbtrg008825m0\n", + " - Queue position is None\n", + "Submitting batch 14\n", + " - Job ID is cmq521rz31fg008xzqdg\n", + " - Queue position is None\n", + "Submitting batch 15\n", + " - Job ID is cmq5230z31fg008xzqeg\n", + " - Queue position is None\n", + "Submitting batch 16\n", + " - Job ID is cmq524gcbtrg008825n0\n", + " - Queue position is None\n", + "Submitting batch 17\n", + " - Job ID is cmq525ryj8fg0080nbjg\n", + " - Queue position is None\n", + "Submitting batch 18\n", + " - Job ID is cmq5278yj8fg0080nbk0\n", + " - Queue position is None\n", + "Submitting batch 19\n", + " - Job ID is cmq528hz28fg0089erdg\n", + " - Queue position is None\n", + "Submitting batch 20\n", + " - Job ID is cmq52ahgh50g008jm04g\n", + " - Queue position is None\n", + "Submitting batch 21\n", + " - Job ID is cmq52bsyj8fg0080nbkg\n", + " - Queue position is None\n", + "Submitting batch 22\n", + " - Job ID is cmq52d9b9x9g0088tg5g\n", + " - Queue position is None\n", + "Submitting batch 23\n", + " - Job ID is cmq52e9b9x9g0088tg60\n", + " - Queue position is None\n", + "Submitting batch 24\n", + " - Job ID is cmq52f9b9x9g0088tg6g\n", + " - Queue position is None\n", + "Submitting batch 25\n", + " - Job ID is cmq52haz31fg008xzqhg\n", + " - Queue position is None\n", + "Submitting batch 26\n", + " - Job ID is cmq52jtgh50g008jm050\n", + " - Queue position is None\n", + "Submitting batch 27\n", + " - Job ID is cmq52ktb9x9g0088tg7g\n", + " - Queue position is None\n", + "Submitting batch 28\n", + " - Job ID is cmq52mtgh50g008jm060\n", + " - Queue position is None\n", + "Submitting batch 29\n", + " - Job ID is cmq52pjcbtrg008825q0\n", + " - Queue position is None\n", + "Submitting batch 30\n", + " - Job ID is cmq52r3b9x9g0088tg80\n", + " - Queue position is None\n", + "Submitting batch 31\n", + " - Job ID is cmq52sbz31fg008xzqjg\n", + " - Queue position is None\n", + "Submitting batch 32\n", + " - Job ID is cmq52tkgh50g008jm06g\n", + " - Queue position is None\n", + "Submitting batch 33\n", + " - Job ID is cmq52vvz31fg008xzqk0\n", + " - Queue position is None\n", + "Submitting batch 34\n", + " - Job ID is cmq52xkgh50g008jm07g\n", + " - Queue position is None\n", + "Submitting batch 35\n", + " - Job ID is cmq52yvz31fg008xzqkg\n", + " - Queue position is None\n", + "Submitting batch 36\n", + " - Job ID is cmq5304yj8fg0080nbn0\n", + " - Queue position is None\n", + "Submitting batch 37\n", + " - Job ID is cmq5314gh50g008jm08g\n", + " - Queue position is None\n" + ] + } + ], + "source": [ + "exp_guadalupe.submit(new_backends[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch 1: JobStatus.DONE\n", + "Batch 2: JobStatus.DONE\n", + "Batch 3: JobStatus.DONE\n", + "Batch 4: JobStatus.DONE\n", + "Batch 5: JobStatus.DONE\n", + "Batch 6: JobStatus.DONE\n", + "Batch 7: JobStatus.DONE\n", + "Batch 8: JobStatus.DONE\n", + "Batch 9: JobStatus.DONE\n", + "Batch 10: JobStatus.DONE\n", + "Batch 11: JobStatus.DONE\n", + "Batch 12: JobStatus.DONE\n", + "Batch 13: JobStatus.DONE\n", + "Batch 14: JobStatus.DONE\n", + "Batch 15: JobStatus.DONE\n", + "Batch 16: JobStatus.DONE\n", + "Batch 17: JobStatus.DONE\n", + "Batch 18: JobStatus.DONE\n", + "Batch 19: JobStatus.DONE\n", + "Batch 20: JobStatus.DONE\n", + "Batch 21: JobStatus.DONE\n", + "Batch 22: JobStatus.DONE\n", + "Batch 23: JobStatus.DONE\n", + "Batch 24: JobStatus.DONE\n", + "Batch 25: JobStatus.DONE\n", + "Batch 26: JobStatus.DONE\n", + "Batch 27: JobStatus.DONE\n", + "Batch 28: JobStatus.DONE\n", + "Batch 29: JobStatus.DONE\n", + "Batch 30: JobStatus.DONE\n", + "Batch 31: JobStatus.DONE\n", + "Batch 32: JobStatus.DONE\n", + "Batch 33: JobStatus.DONE\n", + "Batch 34: JobStatus.DONE\n", + "Batch 35: JobStatus.DONE\n", + "Batch 36: JobStatus.DONE\n", + "Batch 37: JobStatus.DONE\n" + ] + } + ], + "source": [ + "exp_guadalupe.monitor()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Querying IBMQ for results objects for batch 0...\n", + "Querying IBMQ for results objects for batch 1...\n", + "Querying IBMQ for results objects for batch 2...\n", + "Querying IBMQ for results objects for batch 3...\n", + "Querying IBMQ for results objects for batch 4...\n", + "Querying IBMQ for results objects for batch 5...\n", + "Querying IBMQ for results objects for batch 6...\n", + "Querying IBMQ for results objects for batch 7...\n", + "Querying IBMQ for results objects for batch 8...\n", + "Querying IBMQ for results objects for batch 9...\n", + "Querying IBMQ for results objects for batch 10...\n", + "Querying IBMQ for results objects for batch 11...\n", + "Querying IBMQ for results objects for batch 12...\n", + "Querying IBMQ for results objects for batch 13...\n", + "Querying IBMQ for results objects for batch 14...\n", + "Querying IBMQ for results objects for batch 15...\n", + "Querying IBMQ for results objects for batch 16...\n", + "Querying IBMQ for results objects for batch 17...\n", + "Querying IBMQ for results objects for batch 18...\n", + "Querying IBMQ for results objects for batch 19...\n", + "Querying IBMQ for results objects for batch 20...\n", + "Querying IBMQ for results objects for batch 21...\n", + "Querying IBMQ for results objects for batch 22...\n", + "Querying IBMQ for results objects for batch 23...\n", + "Querying IBMQ for results objects for batch 24...\n", + "Querying IBMQ for results objects for batch 25...\n", + "Querying IBMQ for results objects for batch 26...\n", + "Querying IBMQ for results objects for batch 27...\n", + "Querying IBMQ for results objects for batch 28...\n", + "Querying IBMQ for results objects for batch 29...\n", + "Querying IBMQ for results objects for batch 30...\n", + "Querying IBMQ for results objects for batch 31...\n", + "Querying IBMQ for results objects for batch 32...\n", + "Querying IBMQ for results objects for batch 33...\n", + "Querying IBMQ for results objects for batch 34...\n", + "Querying IBMQ for results objects for batch 35...\n", + "Querying IBMQ for results objects for batch 36...\n" + ] + } + ], + "source": [ + "exp_guadalupe.retrieve_results()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(exp_guadalupe.keys)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "data_guadalupe = exp_guadalupe['data']" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "cannot pickle '_thread.lock' object", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/adhumu/pyGSTi/jupyter_notebooks/Tutorials/objects/advanced/MCB_IBMQ23.ipynb Cell 35\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m exp_guadalupe\u001b[39m.\u001b[39;49mwrite(\u001b[39m'\u001b[39;49m\u001b[39mtest_ibmq_experiment_guadalupe\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", + "File \u001b[0;32m~/pyGSTi/pygsti/extras/ibmq/ibmqcore.py:384\u001b[0m, in \u001b[0;36mIBMQExperiment.write\u001b[0;34m(self, dirname)\u001b[0m\n\u001b[1;32m 382\u001b[0m \u001b[39mfor\u001b[39;00m atr \u001b[39min\u001b[39;00m _attribute_to_pickle:\n\u001b[1;32m 383\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(dirname \u001b[39m+\u001b[39m \u001b[39m'\u001b[39m\u001b[39m/ibmqexperiment/\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.pkl\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(atr), \u001b[39m'\u001b[39m\u001b[39mwb\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m f:\n\u001b[0;32m--> 384\u001b[0m _pickle\u001b[39m.\u001b[39;49mdump(\u001b[39mself\u001b[39;49m[atr], f)\n", + "\u001b[0;31mTypeError\u001b[0m: cannot pickle '_thread.lock' object" + ] + } + ], + "source": [ + "exp_guadalupe.write('test_ibmq_experiment_guadalupe')" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# The statistics to compute for each circuit.\n", + "statistics = ['polarization', 'success_probabilities', 'success_counts', 'total_counts', 'two_q_gate_count']\n", + "stats_generator = pygsti.protocols.SimpleRunner(SummaryStats(statistics_to_compute=statistics))\n", + "\n", + "# Computes the stats\n", + "summary_data = stats_generator.run(data_guadalupe)\n", + "\n", + "# Turns this \"summary\" data into a DataFrame\n", + "df = summary_data.to_dataframe('ValueName', drop_columns=['ProtocolName','ProtocolType'])\n", + "\n", + "# Adds a row that tells us which type of circuit the row is for. Will not work if the `keys` in the\n", + "# edesign are changed to not include `RMCs` or `PMCs`. \n", + "df['CircuitType'] = ['RMC' if 'RMCs' in p[0] else 'PMC' for p in df['Path']]\n", + "\n", + "# Redefines \"depth\" as twice what is in the Depth column, because the circuit generation code currently\n", + "# uses a different convention to that used in arXiv:2008.11294.\n", + "df['Depth'] = 2*df['Depth']\n", + "\n", + "# Puts the DataFrame into VBDataFrame object that can be used to create VB plots\n", + "vbdf = pygsti.protocols.VBDataFrame(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pygsti.report.capability_region_plot(vbdf, figsize=(6, 8), scale=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# Extracts the data for a plot like Fig. 2a of arXiv:2008.11294.\n", + "vb_min = {}\n", + "for circuit_type in ('RMC', 'PMC'): \n", + " vbdf1 = vbdf.select_column_value('CircuitType', circuit_type)\n", + " vb_min[circuit_type] = vbdf1.vb_data(metric='polarization', statistic='monotonic_min', no_data_action='min')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_p/xf5xnlx96zn2j4rhy_02hlfm0048tm/T/ipykernel_24318/1059979284.py:5: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n", + " spectral = _cm.get_cmap('Spectral')\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creates the plot like those in Fig. 2a of arXiv:2008.11294. The inner squares\n", + "# are the randomized mirror circuits, and the outer squares are the periodic\n", + "# mirror circuits.\n", + "from matplotlib import cm as _cm\n", + "spectral = _cm.get_cmap('Spectral')\n", + "fig, ax = pygsti.report.volumetric_plot(vb_min['PMC'], scale=1.9, cmap=spectral, figsize=(5.5,8))\n", + "fig, ax = pygsti.report.volumetric_plot(vb_min['RMC'], scale=0.4, cmap=spectral, fig=fig, ax=ax, linescale=0.)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creates a plot like those in Fig. 1d of arXiv:2008.11294. But note\n", + "# that these RMCs don't have the same sampling as those in Fig. 1d:\n", + "# this is just the same type of plot from RMC data, not the same\n", + "# type of RMCs. To get the same color map as in Fig. 1d, set cmap=None\n", + "vbdf1 = vbdf.select_column_value('CircuitType', 'RMC')\n", + "fig, ax = pygsti.report.volumetric_distribution_plot(vbdf1, figsize=(5.5,8), cmap=spectral)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Randomized mirror circuit data can also be used for \"RB\", i.e., estimating an average\n", + "# gate error rate by fitting data to an exponential. Below shows how to do this, and\n", + "# shows the RB error rates versus the number of qubits.\n", + "rb = pygsti.protocols.RB(datatype='adjusted_success_probabilities', defaultfit='A-fixed')" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "rb_results = {}\n", + "r = {}\n", + "for key, subdata in data_guadalupe.items():\n", + " if 'RMCs' in key:\n", + " rb_results[key] = rb.run(subdata)\n", + " n_qubits = int(key.split('-')[0])\n", + " r[n_qubits] = rb_results[key].fits['A-fixed'].estimates['r']" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "plt.plot(widths, [r[w] for w in widths], 'o')\n", + "plt.xlabel('Number of Qubits')\n", + "plt.ylabel(\"RB Error Rate\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pygsti", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}