From 4cb7808259d72b180ef21158a5619ceb2dd7022b Mon Sep 17 00:00:00 2001
From: Ryan Shaffer <3620100+rmshaffer@users.noreply.github.com>
Date: Fri, 29 Sep 2023 12:20:53 -0400
Subject: [PATCH 01/17] Uncomment code after reducing runtime and cost
---
...ing_the_tensor_network_simulator_TN1.ipynb | 420 +++++++++---------
.../2_Graph_optimization_with_QAOA.ipynb | 171 ++++---
2 files changed, 304 insertions(+), 287 deletions(-)
diff --git a/examples/braket_features/Using_the_tensor_network_simulator_TN1.ipynb b/examples/braket_features/Using_the_tensor_network_simulator_TN1.ipynb
index 06714bb10..38cc34a65 100644
--- a/examples/braket_features/Using_the_tensor_network_simulator_TN1.ipynb
+++ b/examples/braket_features/Using_the_tensor_network_simulator_TN1.ipynb
@@ -69,11 +69,11 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {
- "scrolled": false,
"ExecuteTime": {
"end_time": "2023-08-29T21:54:15.038574Z",
"start_time": "2023-08-29T21:54:12.272012Z"
- }
+ },
+ "scrolled": false
},
"outputs": [],
"source": [
@@ -139,11 +139,11 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {
- "scrolled": false,
"ExecuteTime": {
"end_time": "2023-08-29T21:57:31.935938Z",
"start_time": "2023-08-29T21:54:15.062606Z"
- }
+ },
+ "scrolled": false
},
"outputs": [
{
@@ -196,13 +196,14 @@
"T : |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|\n",
"20-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 1670 ms\n",
- "Measurement results: Counter({'00000000000000000000': 51, '11111111111111111111': 49})\n",
+ "This quantum task ran 50 shots and the total runtime was 12548 ms\n",
+ "Measurement results: Counter({'00000000000000000000': 27, '11111111111111111111': 23})\n",
"\n",
"20-qubit SV1 task COMPLETED.\n",
"State vector simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 18 ms\n",
- "Measurement results: Counter({'11111111111111111111': 54, '00000000000000000000': 46})\n",
+ "This quantum task ran 50 shots and the total runtime was 18 ms\n",
+ "Measurement results: Counter({'11111111111111111111': 26, '00000000000000000000': 24})\n",
+ "\n",
"GHZ circuit:\n",
"T : |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|\n",
" \n",
@@ -259,13 +260,14 @@
"T : |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|\n",
"25-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 3088 ms\n",
- "Measurement results: Counter({'1111111111111111111111111': 55, '0000000000000000000000000': 45})\n",
+ "This quantum task ran 50 shots and the total runtime was 12624 ms\n",
+ "Measurement results: Counter({'0000000000000000000000000': 29, '1111111111111111111111111': 21})\n",
"\n",
"25-qubit SV1 task COMPLETED.\n",
"State vector simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 600 ms\n",
- "Measurement results: Counter({'0000000000000000000000000': 57, '1111111111111111111111111': 43})\n",
+ "This quantum task ran 50 shots and the total runtime was 478 ms\n",
+ "Measurement results: Counter({'1111111111111111111111111': 30, '0000000000000000000000000': 20})\n",
+ "\n",
"GHZ circuit:\n",
"T : |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|\n",
" \n",
@@ -332,36 +334,47 @@
"T : |0|1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|\n",
"30-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 19330 ms\n",
- "Measurement results: Counter({'111111111111111111111111111111': 52, '000000000000000000000000000000': 48})\n",
+ "This quantum task ran 50 shots and the total runtime was 2324 ms\n",
+ "Measurement results: Counter({'000000000000000000000000000000': 30, '111111111111111111111111111111': 20})\n",
"\n",
"30-qubit SV1 task COMPLETED.\n",
"State vector simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 15609 ms\n",
- "Measurement results: Counter({'111111111111111111111111111111': 53, '000000000000000000000000000000': 47})\n",
+ "This quantum task ran 50 shots and the total runtime was 15494 ms\n",
+ "Measurement results: Counter({'111111111111111111111111111111': 33, '000000000000000000000000000000': 17})\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"35-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 18789 ms\n",
- "Measurement results: Counter({'11111111111111111111111111111111111': 54, '00000000000000000000000000000000000': 46})\n",
+ "This quantum task ran 50 shots and the total runtime was 2284 ms\n",
+ "Measurement results: Counter({'11111111111111111111111111111111111': 29, '00000000000000000000000000000000000': 21})\n",
+ "\n",
"40-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 3267 ms\n",
- "Measurement results: Counter({'0000000000000000000000000000000000000000': 51, '1111111111111111111111111111111111111111': 49})\n",
+ "This quantum task ran 50 shots and the total runtime was 2412 ms\n",
+ "Measurement results: Counter({'1111111111111111111111111111111111111111': 29, '0000000000000000000000000000000000000000': 21})\n",
+ "\n",
"45-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 19083 ms\n",
- "Measurement results: Counter({'111111111111111111111111111111111111111111111': 51, '000000000000000000000000000000000000000000000': 49})\n",
+ "This quantum task ran 50 shots and the total runtime was 2377 ms\n",
+ "Measurement results: Counter({'000000000000000000000000000000000000000000000': 26, '111111111111111111111111111111111111111111111': 24})\n",
+ "\n",
"50-qubit TN1 task COMPLETED.\n",
"Tensor network simulator:\n",
- "This quantum task ran 100 shots and the total runtime was 3803 ms\n",
- "Measurement results: Counter({'00000000000000000000000000000000000000000000000000': 50, '11111111111111111111111111111111111111111111111111': 50})\n"
+ "This quantum task ran 50 shots and the total runtime was 2434 ms\n",
+ "Measurement results: Counter({'00000000000000000000000000000000000000000000000000': 25, '11111111111111111111111111111111111111111111111111': 25})\n",
+ "\n"
]
}
],
"source": [
"qubit_range = range(20, 31, 5)\n",
"tn_qubit_range = range(35, 51, 5)\n",
- "n_shots = 100\n",
+ "n_shots = 50\n",
"ghz_circs = {}\n",
"sv_tasks = {}\n",
"tn_tasks = {}\n",
@@ -489,11 +502,11 @@
"cell_type": "code",
"execution_count": 6,
"metadata": {
- "scrolled": false,
"ExecuteTime": {
"end_time": "2023-08-29T21:58:24.654423Z",
"start_time": "2023-08-29T21:57:31.957381Z"
- }
+ },
+ "scrolled": false
},
"outputs": [
{
@@ -502,25 +515,24 @@
"text": [
"20-qubit task COMPLETED.\n",
"QFT:\n",
- "This quantum task ran 100 shots and the total runtime was 10269 ms\n",
- "Measurement results: Counter({'11010010111001011011': 1, '00100011100010111011': 1, '01111001110110111000': 1, '11011110001011011101': 1, '01100010011001110011': 1, '11111101110110001100': 1, '10000101110101010110': 1, '00100100000000000011': 1, '01100011010011110110': 1, '00110100111011001001': 1, '01000001101101010110': 1, '00100101001100100111': 1, '01100001011001001000': 1, '11001100110101110101': 1, '10000100011111101000': 1, '00011010001001000001': 1, '10111100010000101010': 1, '10011010111000001011': 1, '01110111011001110000': 1, '10101010011100000100': 1, '11000010100000100101': 1, '10111101111111110000': 1, '00000110110001111010': 1, '11101000101010111110': 1, '11101110100000011110': 1, '10110011010010000100': 1, '10100110010011100010': 1, '10000110100111001010': 1, '10110010010000010110': 1, '01001100110100011010': 1, '01001111110010010010': 1, '10001010110000111100': 1, '11001011110111101110': 1, '10011001010011110000': 1, '01001001001001100000': 1, '10000110101100000011': 1, '01111101111100011000': 1, '10111001001110101100': 1, '00010100101000101100': 1, '10101000001001110111': 1, '10101110100001101100': 1, '11111101111110000101': 1, '00111001101010101100': 1, '00000000100000001110': 1, '11111010110011101010': 1, '11100011111110011011': 1, '10011001011100111111': 1, '00010110111010110011': 1, '00111100101000001100': 1, '11011001000000100111': 1, '00000110101001110100': 1, '11010001000001111111': 1, '10101011111010111011': 1, '10100011100011000100': 1, '00001010000011100000': 1, '00101101000101111101': 1, '11011001110100100110': 1, '00011001111010001011': 1, '01111011101000011000': 1, '01110101111100011000': 1, '11011010111011010101': 1, '10011101101001001101': 1, '10110010001100000001': 1, '11011000000100000010': 1, '10101011101010101111': 1, '01000111000110010000': 1, '01011111101111110000': 1, '00011111000000101010': 1, '11010101010100010101': 1, '10101001000111011000': 1, '01011110001111011100': 1, '10111011001010111001': 1, '11100111100001011111': 1, '00110111000001110111': 1, '00110100000111010110': 1, '10111011101110010100': 1, '01010101110100101101': 1, '11100000110001110000': 1, '01110000111000011111': 1, '11011101000101100101': 1, '01000110111011001101': 1, '01010101100111110101': 1, '01011101111110011000': 1, '11101010100100110011': 1, '00100000010010101000': 1, '10011010110110000100': 1, '01011000100010001001': 1, '11001010001100000111': 1, '00010110010011110010': 1, '00011111001110100011': 1, '10101000101000101001': 1, '10100110111001010010': 1, '10010110101010101010': 1, '01011110111100111110': 1, '01001010011010101011': 1, '00110010001101111111': 1, '10010000111111111001': 1, '00101110110010100001': 1, '00101000111100010111': 1, '11110001011101010010': 1})\n",
+ "This quantum task ran 50 shots and the total runtime was 2210 ms\n",
+ "Measurement results: Counter({'10111011100101010111': 1, '00111001010110001110': 1, '01011010001110100100': 1, '01000110101110001101': 1, '00111111100101100100': 1, '10100011010111101001': 1, '00100000010010100001': 1, '10110011111011100101': 1, '01110011001001110001': 1, '01010011110010110110': 1, '10010000110100010100': 1, '10000101000110011000': 1, '01101011110100010111': 1, '10111111100011010011': 1, '10110000101110011110': 1, '10000111111001001000': 1, '10011010001001001011': 1, '01000010110110101111': 1, '00110011110110011000': 1, '11001101000001101000': 1, '11110110111001101011': 1, '00110010101111011011': 1, '11000100000001110101': 1, '01011011110001111010': 1, '00010101000010111010': 1, '11000001111110111100': 1, '10000110010111101100': 1, '11010110101110110010': 1, '01010111000011010110': 1, '01010100001110100101': 1, '00111100000101101111': 1, '00010101101100001010': 1, '10001110100100011010': 1, '00011000000101100011': 1, '11100011000010010111': 1, '00111011001000001101': 1, '10011101101111111110': 1, '10000100100000000010': 1, '00001001110001000011': 1, '10001001110010001110': 1, '01001111101010010101': 1, '01111110101101010011': 1, '01001000000101000101': 1, '00000001101111111111': 1, '00010001110001111101': 1, '10010000111101101010': 1, '11010101101010110010': 1, '01111010100011000011': 1, '01101111101001001010': 1, '01011110111010011011': 1})\n",
+ "\n",
"30-qubit task COMPLETED.\n",
"QFT:\n",
- "This quantum task ran 100 shots and the total runtime was 7887 ms\n",
- "Measurement results: Counter({'111001110010010011000010011001': 1, '011111010110101000100100101101': 1, '010001100001101111010011101001': 1, '110010100101100100111110000000': 1, '110011111000001101001000011101': 1, '110000100000000010011010111101': 1, '011001110111010101111011100110': 1, '100011110100110000010101011110': 1, '111111101100100011110001110011': 1, '010001111110010001100011011111': 1, '000001110011010101000010101101': 1, '000001011001100000100011110000': 1, '000011111110011010100011000000': 1, '101100001000100110010011001010': 1, '000110101101000111011001101000': 1, '110111110000111000101110100000': 1, '100100100101011100110101100101': 1, '001011011001000110011100110000': 1, '001110011010010111000011011110': 1, '001111101101010011011000001001': 1, '101000011101000000000101110100': 1, '011110110100011100100010011010': 1, '110010100001001001011010110011': 1, '101110001011110001111101110110': 1, '000100011111010000100010000011': 1, '010111100011101010011110100000': 1, '001101000111011001100100010000': 1, '010110001000101010100011011001': 1, '001011111010110101111111100001': 1, '110001111001000101111111000011': 1, '000001000111010111011111000000': 1, '101011000111111000000011100000': 1, '101100110111110011110011100101': 1, '110111011111000010011101101101': 1, '011101100100000110111110001001': 1, '011110000111110100000101101010': 1, '011001001100111111100110011100': 1, '000101100111111110001010000101': 1, '000111110011000100101001101000': 1, '011100111110111100111110000010': 1, '011101101100111101101110000111': 1, '011111011111001101100101001000': 1, '000101110011110101111111100101': 1, '101100110111010001111101000110': 1, '100101100000101011010011111010': 1, '011100110100011111100001010100': 1, '100100010100100111101101011010': 1, '110001001011111001110110010010': 1, '111000101111001000101111011001': 1, '100111101010100100001110001001': 1, '110010100100011000011100010010': 1, '111101001100001110011001011001': 1, '100100101110010111110000011010': 1, '111110000101001100011101001101': 1, '111001110001001111001000000001': 1, '100010110101100100111111011010': 1, '110101101110011010011000111111': 1, '010110010101111001010101100101': 1, '000110001111001100111001010010': 1, '010001110010011111010010011111': 1, '101100000111101011100100001011': 1, '001101100001000100110001111000': 1, '001001101110010010011011100010': 1, '000010011000001101010101111111': 1, '101011111010000110000111001011': 1, '101110101110101000010000101010': 1, '000111010111000111111101100010': 1, '100111110010110010000110011010': 1, '001100101001000100000000010010': 1, '100101010110010011101010010000': 1, '011001001000110001101111000100': 1, '101000000111010010111000110001': 1, '000001010100011100111010100111': 1, '101100101110100100100000111100': 1, '100000101101000011101000100111': 1, '110110100111111000111100011010': 1, '100111001110011011101110010110': 1, '000101001110100000101000000010': 1, '001111000110001000101110010101': 1, '111010101011000110100111101010': 1, '000111001110110011111110100011': 1, '111001101100010101110011111010': 1, '000111101110000100101000101100': 1, '111110111011110101111100101011': 1, '011111110001111100001101100101': 1, '011100111101101010001101001000': 1, '111010011101100110010111000100': 1, '110111101101111000110000000101': 1, '010011001011011100101010111101': 1, '000101111101100001010100100110': 1, '111000110001010010010100010010': 1, '110011001010101001101000000110': 1, '110110101001101011001110011000': 1, '010111111011100100111011000000': 1, '101110011110110110011001000011': 1, '011001010100000101111100100000': 1, '111010101001000011100010010000': 1, '010000101101101010011001111100': 1, '100100111011101100001110100011': 1, '110100011010010000000011000101': 1})\n",
+ "This quantum task ran 50 shots and the total runtime was 13817 ms\n",
+ "Measurement results: Counter({'110000110010001001010100001011': 1, '001011111011010011001100101010': 1, '101001000011011010111010111101': 1, '010001011100111110110110000010': 1, '111000011111100111000001100010': 1, '000001111101111101010001011100': 1, '100000010001101111011010010010': 1, '011111100001010100011001010011': 1, '111100101110111101101111111001': 1, '100110101001000100000111111001': 1, '111110101010100110000101111111': 1, '110001101001000101000100101100': 1, '001011011001011111010000111101': 1, '010000101000001010100001001111': 1, '110001000001011000000000010110': 1, '111100010000101101111010100011': 1, '011101010111111001000011100100': 1, '001100100010001101110100001001': 1, '100001010011010100110000101111': 1, '100001110000110000011010110010': 1, '101101101001101100111010101111': 1, '101100010000000100010110100100': 1, '110001010000001011111010011011': 1, '111011101100000111001000001000': 1, '111100000011011010010101011001': 1, '101010010001011011011100011111': 1, '011010101000100110100000011001': 1, '100011100011110110011010011011': 1, '010011010101101100101001000010': 1, '010101000001100011011100111101': 1, '000000011101001000100011001101': 1, '101101101001101111110000011001': 1, '011011001010011100100011001001': 1, '000100101010110010010011110101': 1, '101011100001101111110110100001': 1, '011001111010111001011011010111': 1, '111010011001010001010011110001': 1, '111001101110100110111111000100': 1, '010111110011111001100110000011': 1, '100001000101001101110011100100': 1, '000111010000111110111101111011': 1, '011001000000110000000101101001': 1, '100000100100111010100001111010': 1, '000001101001100101111100011011': 1, '001101110010000101101100011000': 1, '101011100001001000001000100000': 1, '111000111011001101110101110011': 1, '110000010011100010011010110000': 1, '110100110000111011010111110100': 1, '011000010110101101000100011101': 1})\n",
+ "\n",
"40-qubit task COMPLETED.\n",
"QFT:\n",
- "This quantum task ran 100 shots and the total runtime was 16919 ms\n",
- "Measurement results: Counter({'0000001001000100111110000101001011000010': 1, '0010111100101100010100010101100011011000': 1, '0111010100000001111001010001010100111101': 1, '1000111110011011111110110010001001010011': 1, '0001110101001000110101100010101010001111': 1, '1110000000101011001100100001001011101000': 1, '1101010111010101101000111000110000010001': 1, '0000111001011111001001001000010110101000': 1, '1100000110000010101011011000111100111000': 1, '0010100110001111101100011100100110010000': 1, '0001001100010100010101101110101001011101': 1, '0100101010111111000101111101010001101110': 1, '1010001100100110011101110000001100010000': 1, '1101101101000011010111011011010011001010': 1, '0001101101100100110100010010011011101111': 1, '0101110011111100111011010011001010000001': 1, '1111101001101010110110010101001000011101': 1, '0111110001011110010011110100100100001101': 1, '1011000110010111110111010111011000010111': 1, '1101001011000100110101111001110100110000': 1, '1011011001111111101101010111100101000001': 1, '1100011111110000101010110110100111010101': 1, '0101100111110001110010100101000001011011': 1, '1101011011111010111100111001000000110111': 1, '0000110000001110010111001010110111011001': 1, '1001001011111110100111111111001110100011': 1, '0001010010011111010010001101000000001100': 1, '1111001001011011001100000010100001010011': 1, '1000011000011001010111100100010111010110': 1, '0010100000011000100000001010100111000110': 1, '1010000111100011111010101100110110101100': 1, '0010011001011001001010010101101001111000': 1, '0001111000001000010011001000011111100111': 1, '1111000010101101000001011011001110001001': 1, '0011010000010100110001000000100011111110': 1, '1110011111110110100111110100110101110000': 1, '0111011001000100010111100000011011001101': 1, '0100100001000110100110110111000101010001': 1, '1111010100011110000101101110010001011001': 1, '1100100110110110100111001011000011000110': 1, '1011010110111110100100100000101110111011': 1, '0010101011010010101001000111011001000100': 1, '0101011100001001010010000111001111101111': 1, '1110011111000000111101011010111011111010': 1, '1111110011001101011101010001001110010100': 1, '1111101010101011101000111111111100001100': 1, '1010101101000001101001000001000110010100': 1, '0101011000010000101110000100000111010000': 1, '1011000011101110100110111001111100001010': 1, '0010010110010010110111000101110000110110': 1, '0000101010000100101101000001101000100010': 1, '1011001100001101101010011011001101010111': 1, '1111001000110111110011010101011000111011': 1, '1101110011110100100100010110101100111000': 1, '1111010001111011011010010010010011110110': 1, '1011101011011010010100100001101011010111': 1, '0011010000010110110101101101010011111100': 1, '0011001111011111010011000011111000111101': 1, '0100101011000000111011101111000010000011': 1, '0110101001111100101111110011000111100110': 1, '1011110101111000011101000011111000111001': 1, '0001000010001000110101101100010111000101': 1, '0111010100011001000001011111101001110000': 1, '1100010001001110010011000001011000011010': 1, '1011110111111101010110111101111010111110': 1, '1001000101010011000000011100000011101010': 1, '0111101100100100011111110100001010101111': 1, '0110010011101101111010011001110010110101': 1, '0111110000111101001110110001001111011100': 1, '1001011101010101110101011111101110100011': 1, '0111110100011011111100011001001101010000': 1, '0100010100000101001101011101100101100100': 1, '1101110111110000101000100010010110010011': 1, '1101010000111010011000101111000010110001': 1, '0111110010011010110010001110101010011001': 1, '0110010110101000100001001101000101011110': 1, '0101000100011111100011010001111111100100': 1, '0011101010101100000110111011100010111101': 1, '1101000001000101011010110100111101010100': 1, '0110010001011010011111001101011010111000': 1, '0001010100110100100000001110001010111011': 1, '1001011110101110001101100111010111110001': 1, '0100010100111110110111110000000001111110': 1, '1100110011011011110100111101111011001010': 1, '1000011011011000001001010110101001011000': 1, '0010101110010011010101000001110011110101': 1, '1001001110101110101010011000010111011110': 1, '0011000011010001100100001000100111110010': 1, '0011100110010000101010010000011100100111': 1, '1000100011100111010101111100011001111000': 1, '0001110001101110000101011110101010011110': 1, '0111000010101100010000011101001001001010': 1, '0010001100111001000010110011100010000010': 1, '0111011110110111000101100110001111101011': 1, '1100110101111110010100000000111011101100': 1, '0100101100110111010000110010000000001100': 1, '1000001011011011010011001000000010000100': 1, '0111010111011001011010011001110100000010': 1, '0001011000101111010110001110100000101111': 1, '0110011010001100010101011000111010001100': 1})\n",
- "50-qubit task COMPLETED.\n",
- "QFT:\n",
- "This quantum task ran 100 shots and the total runtime was 39142 ms\n",
- "Measurement results: Counter({'00001000100111000111111000000000011100100100011011': 1, '01101100001000101100011011000011011001111011011100': 1, '00010010110111100010111111000001000011000110010101': 1, '10001101011011010100111001010011010101110001100110': 1, '11100001100001100010000010101000110110011110101100': 1, '11101010101011100011001001101000100101010110010101': 1, '00111010110001101010011001111110101111110010101011': 1, '01100111001001110111010111011100011011101111011111': 1, '01011111010000001000011111010111110100101000111010': 1, '11000111100100011011000010011100011001001000100011': 1, '01111001110111000001001000111010110010011001001100': 1, '10101101111111001001000100001000110111010101101100': 1, '00110111011000011101101011101011010011110010110111': 1, '11000001100001000110110000111111100110101011010100': 1, '00101001010100111001000000101110100100010011011111': 1, '01100000110110110111011111100011111111100001110000': 1, '10100110101111100101010000011111000011000110101110': 1, '00010110110010011100001011000010011100110011110100': 1, '10101011000101001001010101010011010101011001111111': 1, '01001111000110110001000101001000101010111001101111': 1, '10111101111111111101011101000110010001110111101111': 1, '01001011011011101101110010101001000100111000001111': 1, '00110011011000001011111010100010000010111111011100': 1, '01100111000111110001000100001110010100111100000111': 1, '00100010000111011100000001000101000001100001110100': 1, '11011100100001001000001011010011000011000010000010': 1, '11111001100101110110101111011101000101011000010110': 1, '11100010010101111101001011100100101100010011101101': 1, '10110010011000000011000011000101010100110000100011': 1, '11000101100011110101101011101100101000000110010000': 1, '10101010100001111110000110110101101101001110011111': 1, '11110111110000110100000000111010011010001111101101': 1, '00010000010010111110010010001001000000110000110111': 1, '00010000110111010010110001111111111100111001010101': 1, '11110110001000010110011011011110011110000000101000': 1, '10011010101101001010101110000101110101111011101011': 1, '01111001111010001111011000111010111011100011110010': 1, '10001111000111111001000010100110110000111000010000': 1, '01010110101111100000000101011000011110111010011101': 1, '00010000101101011010101100010100101110100000101100': 1, '10100001110100100010001100011100100010111000011110': 1, '00111001110100010100010101000010011111010101100001': 1, '10011100100001010101011000000011101101100001001000': 1, '01010101011010010011000101001011100001010001110000': 1, '01011010001010110010010011110000101110100010111010': 1, '10011110110010011011110111111111011111010011001101': 1, '00000101001001011100111000111100101001001011000001': 1, '01110010010000110000100111001100010000000101011100': 1, '00010101010110000100011101100001110010001111000010': 1, '01110111100001001010000010001100010010010110110110': 1, '11110101110011000101001110010100000001010011010101': 1, '01010110101100110000111111110110011100011000011100': 1, '10100011001001110101011010100001010101111110000010': 1, '01001101111101100010100001100100100111101101100010': 1, '11011111100011010111011100011101010011101111111101': 1, '10000010110001001110001101110000001000000010100101': 1, '01111001011101111101001100010110111110000101001000': 1, '00110101111001110010010111110000000010101001010101': 1, '00011100000010011110111100011001000110111000011100': 1, '11110001101111001011000010101000001100000011010100': 1, '01000101110001101101011111001100110011100110100000': 1, '11011011011000111110100010100111111001110110100011': 1, '00110101101010010010000101010000110110111011111001': 1, '01001111101001100000100101110111010101001000011000': 1, '01111111001101011000000111110101000011010001111010': 1, '00100111111101010011000011110001111110000110011101': 1, '11101001010101111101101001000111111100101101101100': 1, '10110010101001111100111110100101110000000000110000': 1, '01101110111101010000010011000101010001000011000100': 1, '10011001101101101101000111010101011010010011000000': 1, '01011000110111000100001011111011111101000000111100': 1, '11010100000011101001100010011010111111001111101010': 1, '11010010001110101001111101110001100000100010011011': 1, '01011110010011101010101110110111101010010110100100': 1, '00000011100101000111000110100110001111100010010110': 1, '01011010101011101010000010011000010011111001101110': 1, '10110110100111010001111110111001111000010110100001': 1, '11111110001011101101100100111100011011110011100001': 1, '10110111001000111111101100000111101100100001110111': 1, '11101100001101111011101100011100111101111000110100': 1, '01000111000110001010010100101110000100001000001111': 1, '01100101101101001110010111110100110010111000100111': 1, '00001111101100011110010010011010000010110101010011': 1, '10110001111100101001101101000010011010001010011001': 1, '10111111101010001101011000110101000001110001100101': 1, '01010010000101001110011011011011010100011111011100': 1, '11011001000011101000100001010110010100011100110001': 1, '10001100001011111101111110000110001101001011101001': 1, '11111101100010011111101011000010101010101011100110': 1, '00011100001010011011001110001111001000010011010110': 1, '11111101001001111001011010000111100000111110110011': 1, '01101110101100111111101100110111001001111100110110': 1, '01001011000101010100110110110110100010001100011001': 1, '10001110100011100001010111110111101100001110100110': 1, '11011110010100100011010110010011110011010001110111': 1, '11101110011001110110000110101111000011111100010110': 1, '10101001011000001010100001011101101010010011000001': 1, '10101001010001101011011010110100101100011011110010': 1, '11000111110010001110101010100100000111001100100100': 1, '00111110100010000011001000110101001011011101111110': 1})\n"
+ "This quantum task ran 50 shots and the total runtime was 20795 ms\n",
+ "Measurement results: Counter({'1101001000110111001111100110100001001000': 1, '0000111010001100100100100011000111001101': 1, '0111101111110001010110001010110011110011': 1, '0010001110011000111000010011011010111010': 1, '0000010111101100100010100100010111000111': 1, '1011100100101011100000101110110100010110': 1, '1110110100011100101101100000110111000010': 1, '1101001111100001110011001101110101111101': 1, '0110000100111100110111001000110001001000': 1, '1100111110010011100000101110101100000000': 1, '0011010111011101011111011110100010101110': 1, '1110111101110001101001011101110110001100': 1, '1100010100000011100111111111100100110110': 1, '0000010010101111100100110101110101010011': 1, '1111111101100000011010110100000001100101': 1, '1111100001000110001001111110101111111001': 1, '0110110011100011000011001001011110000111': 1, '1110001011100010001001110000101101110010': 1, '0101100001111110010000010101111000111111': 1, '1010000101100011110011101111001100011010': 1, '1011110001011011000011100100011000001001': 1, '1011100101000100010010110000010000000100': 1, '0110110101110011111000110100100011001101': 1, '0111100101000011101011001101001011100011': 1, '0110011000010001110101001001100110011100': 1, '0110101100010000010000010001001111011010': 1, '1011001001111110101100100010010101010111': 1, '0011101110000111010011010100100101100001': 1, '1100010001011001000011111111101101110001': 1, '0001111111001101101110101001010010100100': 1, '0011100010111111001000001000110001111001': 1, '0010110000011100000110101101101110000001': 1, '0011100101001111101010000001110111101111': 1, '1100010011011000100101100111100100001111': 1, '0110110011010011100110010010101110110000': 1, '0100000100000000011101101110101111111100': 1, '1110011000010110111100101001001000011111': 1, '0110110000101000010100100010110000111110': 1, '1100001101000011111010011010101111110110': 1, '0001111011011001011100100100010110000000': 1, '0010100000000011001100111000101001000001': 1, '1010101100101111011011101000010001011100': 1, '0101110110000101101111111000101110101111': 1, '0000001001011010000000000101001100111010': 1, '0001011110000111000111110001100111110010': 1, '1000101111111001000011000000000100000111': 1, '0111001010010011110110010000101000010000': 1, '0111100100111001101111111101110100110100': 1, '0000111111100000111111010110111101000110': 1, '1100000101111101100101101001011001000010': 1})\n",
+ "\n"
]
}
],
"source": [
- "qubit_range = range(20, 51, 10)\n",
+ "qubit_range = range(20, 41, 10)\n",
"tn_tasks = {}\n",
"tn_results = {}\n",
"for num_qubits in qubit_range:\n",
@@ -540,7 +552,6 @@
" # get the measurement counts\n",
" tn_counts = tn_results[num_qubits].measurement_counts\n",
" \n",
- "\n",
" print('{}-qubit task {}.'.format(num_qubits,tn_status))\n",
" print('QFT:')\n",
" print('This quantum task ran {} shots and the total runtime was {} ms'.format(tn_num_shots,tn_runtime))\n",
@@ -614,11 +625,11 @@
"cell_type": "code",
"execution_count": 8,
"metadata": {
- "scrolled": false,
"ExecuteTime": {
"end_time": "2023-08-29T21:58:24.702643Z",
"start_time": "2023-08-29T21:58:24.696509Z"
- }
+ },
+ "scrolled": false
},
"outputs": [
{
@@ -627,77 +638,77 @@
"text": [
"\n",
"HAYDEN PRESKILL CIRCUIT WITH 5 GATES:\n",
- "T : | 0 | 1 | 2 |3|\n",
- " \n",
- "q1 : -Rz(0.44)---------------------\n",
- " \n",
- "q2 : -C----------------------------\n",
- " | \n",
- "q3 : -Z--------Ry(0.60)-Ry(0.74)-C-\n",
- " | \n",
- "q4 : ----------------------------Z-\n",
+ "T : | 0 | 1 |2|\n",
+ " \n",
+ "q0 : -C-------------------\n",
+ " | \n",
+ "q1 : -Z-------------------\n",
+ " \n",
+ "q2 : -Ry(6.24)------------\n",
+ " \n",
+ "q3 : -C--------Rx(2.63)-C-\n",
+ " | | \n",
+ "q4 : -Z-----------------Z-\n",
"\n",
- "T : | 0 | 1 | 2 |3|\n",
+ "T : | 0 | 1 |2|\n",
"\n",
"HAYDEN PRESKILL CIRCUIT WITH 10 GATES:\n",
- "T : | 0 |1| 2 |3|4| 5 |6|\n",
- " \n",
- "q0 : ----------C-Rz(1.86)----------------\n",
- " | \n",
- "q1 : -Rx(1.72)-Z-C-----------------------\n",
- " | \n",
- "q2 : ------------Z--------C-H----------C-\n",
- " | | \n",
- "q3 : -C-------------------Z-C-Rz(3.70)-Z-\n",
- " | | \n",
- "q4 : -Z---------------------Z------------\n",
+ "T : |0| 1 |2|3| 4 | 5 |\n",
+ " \n",
+ "q1 : --------------C-Rx(4.33)-Rx(0.81)-\n",
+ " | \n",
+ "q2 : -H----------C-Z-C-----------------\n",
+ " | | \n",
+ "q3 : -C-H--------Z-C-Z-----------------\n",
+ " | | \n",
+ "q4 : -Z-Rx(1.79)---Z-------------------\n",
"\n",
- "T : | 0 |1| 2 |3|4| 5 |6|\n",
+ "T : |0| 1 |2|3| 4 | 5 |\n",
"\n",
"HAYDEN PRESKILL CIRCUIT WITH 15 GATES:\n",
- "T : |0|1|2| 3 |4| 5 | 6 |\n",
+ "T : | 0 |1| 2 |3| 4 |5|6|\n",
" \n",
- "q0 : -C----------------C--------C--------\n",
- " | | | \n",
- "q1 : -Z-C------------C-Z--------Z--------\n",
- " | | \n",
- "q2 : ---Z-C-Rx(1.33)-Z-Ry(3.61)-Rz(2.73)-\n",
- " | \n",
- "q3 : -C---Z-Rz(4.31)-C-------------------\n",
- " | | \n",
- "q4 : -Z-H------------Z-H--------Rz(5.25)-\n",
+ "q0 : ---------------------C-Rx(2.12)-----\n",
+ " | \n",
+ "q1 : -Ry(4.60)-H-H--------Z-C--------C---\n",
+ " | | \n",
+ "q2 : -C--------C------------Z--------Z-H-\n",
+ " | | \n",
+ "q3 : -Z--------Z-Ry(3.82)-C-C------------\n",
+ " | | \n",
+ "q4 : -Rz(5.56)-H----------Z-Z------------\n",
"\n",
- "T : |0|1|2| 3 |4| 5 | 6 |\n",
+ "T : | 0 |1| 2 |3| 4 |5|6|\n",
"\n",
"HAYDEN PRESKILL CIRCUIT WITH 20 GATES:\n",
- "T : | 0 | 1 | 2 |3|4|5| 6 | 7 | 8 |9|10|11|12|13| 14 |15|\n",
- " \n",
- "q0 : ----------------------------C----------------------------------------------------------\n",
- " | \n",
- "q1 : -H--------Ry(0.22)-Rz(5.02)-Z-C-C-Rx(5.17)-C-------------------C--C--------------------\n",
- " | | | | | \n",
- "q2 : -Rx(0.17)---------------------Z-Z-C--------Z-----------------C-Z--Z--C-----------------\n",
- " | | | \n",
- "q3 : ----------------------------------Z--------Ry(0.16)-Ry(5.64)-Z-------Z--C--Ry(2.37)-C--\n",
- " | | \n",
- "q4 : -Rz(1.49)---------------------------------------------------------------Z-----------Z--\n",
+ "T : | 0 |1| 2 | 3 | 4 |5| 6 |7|8|9|\n",
+ " \n",
+ "q0 : -Ry(5.21)-C-Rx(4.17)---------------------C--------------\n",
+ " | | \n",
+ "q1 : -Rx(2.00)-Z----------C-----------------C-Z----------C---\n",
+ " | | | \n",
+ "q2 : -Ry(3.59)-C-Rz(0.74)-Z--------Rz(4.65)-Z-Ry(1.62)-C-Z-C-\n",
+ " | | | \n",
+ "q3 : ----------Z-H--------Ry(1.07)-C-------------------Z-C-Z-\n",
+ " | | \n",
+ "q4 : -Rx(5.20)---------------------Z---------------------Z---\n",
"\n",
- "T : | 0 | 1 | 2 |3|4|5| 6 | 7 | 8 |9|10|11|12|13| 14 |15|\n",
+ "T : | 0 |1| 2 | 3 | 4 |5| 6 |7|8|9|\n",
"\n",
"HAYDEN PRESKILL CIRCUIT WITH 25 GATES:\n",
- "T : | 0 | 1 | 2 | 3 |4|5|6|7| 8 |9| 10 |11|12|\n",
- " \n",
- "q0 : -Rz(0.66)-Rx(0.16)-Ry(0.10)-Rx(0.85)-----------------------------------\n",
- " \n",
- "q1 : -Rz(1.28)-H-------------------------------------------C----------------\n",
- " | \n",
- "q2 : ----------C--------H--------Ry(2.26)-C-------C--------Z-Rz(5.43)-------\n",
- " | | | \n",
- "q3 : -C--------Z--------H-----------------Z-H-C-C-Z--------C-C--------C--H--\n",
- " | | | | | | \n",
- "q4 : -Z--------Rz(0.33)-----------------------Z-Z-Rz(2.78)-Z-Z--------Z--H--\n",
+ "T : |0|1|2| 3 | 4 | 5 |6|7|8|9|10|11| 12 | 13 |14|\n",
+ " \n",
+ "q0 : -H-C------------C----------------------------------------------------\n",
+ " | | \n",
+ "q1 : ---Z-C-Rx(1.07)-Z--------C--------C-H--------------------------------\n",
+ " | | | \n",
+ "q2 : ---C-Z-------------------Z--------Z-C-C---C--C--Rz(1.98)-Rx(6.11)----\n",
+ " | | | | | \n",
+ "q3 : -H-Z-C-C----------------------------Z-Z-H-Z--Z--C--------H--------C--\n",
+ " | | | | \n",
+ "q4 : -H---Z-Z--------Rx(4.46)-Rx(3.80)---------------Z-----------------Z--\n",
"\n",
- "T : | 0 | 1 | 2 | 3 |4|5|6|7| 8 |9| 10 |11|12|\n"
+ "T : |0|1|2| 3 | 4 | 5 |6|7|8|9|10|11| 12 | 13 |14|\n"
]
}
],
@@ -719,59 +730,66 @@
"We will examine runtimes for various depths of circuits at relatively low (for TN1) qubit counts. This is to ensure that our job finishes in a reasonable amount of time. We'll examine the measurement counts at the end of each simulation. Because these are random circuits, we should not expect to see the measurement counts highly concentrated in a few outcomes."
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "
\n",
- "Caution: Running the following cell will take about 2 minutes. Only uncomment it if you are happy to wait.\n",
- "
"
- ]
- },
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
- "scrolled": false,
"ExecuteTime": {
"end_time": "2023-08-29T21:58:24.709311Z",
"start_time": "2023-08-29T21:58:24.702265Z"
- }
+ },
+ "scrolled": false
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20-qubit 48-depth task COMPLETED.\n",
+ "Hayden-Preskill circuit:\n",
+ "This task ran 5 shots and the total runtime was 6795 ms\n",
+ "Measurement results: Counter({'11010111111101110111': 1, '11000011111000101101': 1, '11100110100100010110': 1, '11000010001100101111': 1, '10000001011010111100': 1})\n",
+ "\n",
+ "20-qubit 69-depth task COMPLETED.\n",
+ "Hayden-Preskill circuit:\n",
+ "This task ran 5 shots and the total runtime was 22773 ms\n",
+ "Measurement results: Counter({'00110010000101010000': 1, '00010001010101000001': 1, '11011100010101000111': 1, '11101100011011001100': 1, '10001110000001110111': 1})\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
- "#num_qubits = 50\n",
- "#n_shots = 10\n",
- "#gate_range = range(500, 1001, 250)\n",
- "#tn_tasks = {}\n",
- "#tn_results = {}\n",
- "#for gates in gate_range:\n",
- "# # construct HP circuit\n",
- "# circ = Circuit()\n",
- "# # ensure the HP circuit is runnable -- circuits must have depth <= 100\n",
- "# while True:\n",
- "# circ = local_Hayden_Preskill(num_qubits, gates)\n",
- "# if circ.depth <= 100:\n",
- "# break\n",
- "# tn_tasks[circ.depth] = tn_device.run(circ, shots=n_shots)\n",
- "#\n",
- "#for depth in tn_tasks.keys():\n",
- "# tn_status = tn_tasks[depth].state()\n",
- "# while tn_status != 'COMPLETED':\n",
- "# tn_status = tn_tasks[depth].state()\n",
- "#\n",
- "# tn_results[depth] = tn_tasks[depth].result()\n",
- "# # get the running time of the task\n",
- "# tn_runtime = tn_results[depth].additional_metadata.simulatorMetadata.executionDuration\n",
- "#\n",
- "# # get the measurement counts\n",
- "# tn_counts = tn_results[depth].measurement_counts\n",
- "# \n",
- "#\n",
- "# print('{}-qubit {}-depth task {}.'.format(num_qubits,depth,tn_status))\n",
- "# print('Hayden-Preskill circuit:')\n",
- "# print('This task ran {} shots and the total runtime was {} ms'.format(n_shots,tn_runtime))\n",
- "# print(\"Measurement results: {}\\n\".format(tn_counts))"
+ "num_qubits = 20\n",
+ "n_shots = 5\n",
+ "gate_range = [250, 500]\n",
+ "tn_tasks = {}\n",
+ "tn_results = {}\n",
+ "for gates in gate_range:\n",
+ " # construct HP circuit\n",
+ " circ = Circuit()\n",
+ " # ensure the HP circuit is runnable -- circuits must have depth <= 100\n",
+ " while True:\n",
+ " circ = local_Hayden_Preskill(num_qubits, gates)\n",
+ " if circ.depth <= 100:\n",
+ " break\n",
+ " tn_tasks[circ.depth] = tn_device.run(circ, shots=n_shots)\n",
+ "\n",
+ "for depth in tn_tasks.keys():\n",
+ " tn_status = tn_tasks[depth].state()\n",
+ " while tn_status != 'COMPLETED':\n",
+ " tn_status = tn_tasks[depth].state()\n",
+ "\n",
+ " tn_results[depth] = tn_tasks[depth].result()\n",
+ " # get the running time of the task\n",
+ " tn_runtime = tn_results[depth].additional_metadata.simulatorMetadata.executionDuration\n",
+ "\n",
+ " # get the measurement counts\n",
+ " tn_counts = tn_results[depth].measurement_counts\n",
+ " \n",
+ " print('{}-qubit {}-depth task {}.'.format(num_qubits,depth,tn_status))\n",
+ " print('Hayden-Preskill circuit:')\n",
+ " print('This task ran {} shots and the total runtime was {} ms'.format(n_shots,tn_runtime))\n",
+ " print(\"Measurement results: {}\\n\".format(tn_counts))"
]
},
{
@@ -792,15 +810,6 @@
""
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "\n",
- "Caution: Running the following cell will take about 3 minutes. Only uncomment it if you are happy to wait.\n",
- "
"
- ]
- },
{
"cell_type": "code",
"execution_count": 10,
@@ -810,51 +819,64 @@
"start_time": "2023-08-29T21:58:24.709746Z"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GHZ task ran 5 shots and the total runtime was 1696 ms\n",
+ "QFT task ran 5 shots and the total runtime was 2940 ms\n",
+ "HP task ran 5 shots and the total runtime was 40343 ms\n",
+ "GHZ task ran 20 shots and the total runtime was 1315 ms\n",
+ "QFT task ran 20 shots and the total runtime was 2916 ms\n",
+ "HP task ran 20 shots and the total runtime was 38092 ms\n"
+ ]
+ }
+ ],
"source": [
- "#num_qubits = 30\n",
- "#ghz_circ = ghz_circuit(num_qubits)\n",
- "#qft_circ = qft(range(num_qubits))\n",
- "#hp_circ = Circuit()\n",
- "#while True:\n",
- "# hp_circ = local_Hayden_Preskill(num_qubits, 750)\n",
- "# if circ.depth <= 100:\n",
- "# break\n",
- "#ghz_tasks = {}\n",
- "#ghz_results = {}\n",
- "#qft_tasks = {}\n",
- "#qft_results = {}\n",
- "#hp_tasks = {}\n",
- "#hp_results = {}\n",
- "#for n_shots in [10, 40]:\n",
- "# ghz_tasks[n_shots] = tn_device.run(ghz_circ, shots=n_shots)\n",
- "# qft_tasks[n_shots] = tn_device.run(qft_circ, shots=n_shots)\n",
- "# hp_tasks[n_shots] = tn_device.run(hp_circ, shots=n_shots)\n",
- "#\n",
- "#for n_shots in [10, 40]:\n",
- "# ghz_status = ghz_tasks[n_shots].state()\n",
- "# while ghz_status != 'COMPLETED':\n",
- "# ghz_status = ghz_tasks[n_shots].state()\n",
- "#\n",
- "# qft_status = qft_tasks[n_shots].state()\n",
- "# while qft_status != 'COMPLETED':\n",
- "# qft_status = qft_tasks[n_shots].state()\n",
- "#\n",
- "# hp_status = hp_tasks[n_shots].state()\n",
- "# while hp_status != 'COMPLETED':\n",
- "# hp_status = hp_tasks[n_shots].state()\n",
- "#\n",
- "# ghz_results[n_shots] = ghz_tasks[n_shots].result()\n",
- "# qft_results[n_shots] = qft_tasks[n_shots].result()\n",
- "# hp_results[n_shots] = hp_tasks[n_shots].result()\n",
- "# # get the running time of the task\n",
- "# ghz_runtime = ghz_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
- "# qft_runtime = qft_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
- "# hp_runtime = hp_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
- "#\n",
- "# print('GHZ task ran {} shots and the total runtime was {} ms'.format(n_shots,ghz_runtime))\n",
- "# print('QFT task ran {} shots and the total runtime was {} ms'.format(n_shots,qft_runtime))\n",
- "# print('HP task ran {} shots and the total runtime was {} ms'.format(n_shots,hp_runtime))"
+ "num_qubits = 25\n",
+ "ghz_circ = ghz_circuit(num_qubits)\n",
+ "qft_circ = qft(range(num_qubits))\n",
+ "hp_circ = Circuit()\n",
+ "while True:\n",
+ " hp_circ = local_Hayden_Preskill(num_qubits, 750)\n",
+ " if circ.depth <= 100:\n",
+ " break\n",
+ "ghz_tasks = {}\n",
+ "ghz_results = {}\n",
+ "qft_tasks = {}\n",
+ "qft_results = {}\n",
+ "hp_tasks = {}\n",
+ "hp_results = {}\n",
+ "for n_shots in [5, 20]:\n",
+ " ghz_tasks[n_shots] = tn_device.run(ghz_circ, shots=n_shots)\n",
+ " qft_tasks[n_shots] = tn_device.run(qft_circ, shots=n_shots)\n",
+ " hp_tasks[n_shots] = tn_device.run(hp_circ, shots=n_shots)\n",
+ "\n",
+ "for n_shots in [5, 20]:\n",
+ " ghz_status = ghz_tasks[n_shots].state()\n",
+ " while ghz_status != 'COMPLETED':\n",
+ " ghz_status = ghz_tasks[n_shots].state()\n",
+ "\n",
+ " qft_status = qft_tasks[n_shots].state()\n",
+ " while qft_status != 'COMPLETED':\n",
+ " qft_status = qft_tasks[n_shots].state()\n",
+ "\n",
+ " hp_status = hp_tasks[n_shots].state()\n",
+ " while hp_status != 'COMPLETED':\n",
+ " hp_status = hp_tasks[n_shots].state()\n",
+ "\n",
+ " ghz_results[n_shots] = ghz_tasks[n_shots].result()\n",
+ " qft_results[n_shots] = qft_tasks[n_shots].result()\n",
+ " hp_results[n_shots] = hp_tasks[n_shots].result()\n",
+ " # get the running time of the task\n",
+ " ghz_runtime = ghz_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
+ " qft_runtime = qft_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
+ " hp_runtime = hp_results[n_shots].additional_metadata.simulatorMetadata.executionDuration\n",
+ "\n",
+ " print('GHZ task ran {} shots and the total runtime was {} ms'.format(n_shots,ghz_runtime))\n",
+ " print('QFT task ran {} shots and the total runtime was {} ms'.format(n_shots,qft_runtime))\n",
+ " print('HP task ran {} shots and the total runtime was {} ms'.format(n_shots,hp_runtime))"
]
},
{
@@ -872,9 +894,9 @@
"output_type": "stream",
"text": [
"Quantum Task Summary\n",
- "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 300, 'tasks': {'COMPLETED': 3}, 'execution_duration': datetime.timedelta(seconds=16, microseconds=227000), 'billed_execution_duration': datetime.timedelta(seconds=21, microseconds=609000)}, 'arn:aws:braket:::device/quantum-simulator/amazon/tn1': {'shots': 1100, 'tasks': {'COMPLETED': 11}, 'execution_duration': datetime.timedelta(seconds=143, microseconds=247000), 'billed_execution_duration': datetime.timedelta(seconds=144, microseconds=577000)}}\n",
+ "{'arn:aws:braket:::device/quantum-simulator/amazon/sv1': {'shots': 150, 'tasks': {'COMPLETED': 3}, 'execution_duration': datetime.timedelta(seconds=15, microseconds=990000), 'billed_execution_duration': datetime.timedelta(seconds=21, microseconds=494000)}, 'arn:aws:braket:::device/quantum-simulator/amazon/tn1': {'shots': 585, 'tasks': {'COMPLETED': 18}, 'execution_duration': datetime.timedelta(seconds=190, microseconds=695000), 'billed_execution_duration': datetime.timedelta(seconds=197, microseconds=787000)}}\n",
"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n",
- "Estimated cost to run this example: 0.690 USD\n"
+ "Estimated cost to run this example: 0.933 USD\n"
]
}
],
@@ -888,7 +910,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3.8.10 ('venv': venv)",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -902,7 +924,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.10"
+ "version": "3.11.4"
},
"vscode": {
"interpreter": {
diff --git a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb
index 1a36f7627..dc228503b 100644
--- a/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb
+++ b/examples/pennylane/2_Graph_optimization_with_QAOA/2_Graph_optimization_with_QAOA.ipynb
@@ -26,7 +26,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -42,9 +41,9 @@
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
- "