{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# High priority jobs\n",
"This example demonstrates how high-priority tokens can be used within Artemis. It enables jobs to skip to the front of the queue, which may be useful when trying to perform real-time computations on the system that require submission of jobs based on the results from previous jobs. This capability can be assigned to users by your system administrator for a limited period of time, but should only be used where necessary for a computation.\n",
"\n",
"In this notebook, it is assumed you have the high-priority capability, but if not then it should be possible to follow along without this."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Token creation\n",
"\n",
"To submit high-priority jobs to the scheduler, an access token with the high-priority attribute needs to be created. This token can be used at the same time as other normal-priority tokens to switch between scheduling mode as required.\n",
"\n",
"To create a high-priority token, users must be assigned this capability by their administrator. Once done, when creating a new token the high-priority field can be set to yes, which enables the capability for a user.\n",
"\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Job submission\n",
"Once created, this token can then be used the same way as any other token would be, using the token management system to set, save & load as required."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import lightworks as lw\n",
"from lightworks import remote\n",
"\n",
"remote.token.set(\"HIGH_PRIORITY_TOKEN\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A basic sampling job can then be created and submitted. If there are other jobs in the queue when the job is submitted then these will be bypassed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"qpu = remote.QPU(\"Artemis\")\n",
"\n",
"sampler = lw.Sampler(\n",
" circuit=lw.Unitary(lw.random_unitary(6)),\n",
" input_state=lw.State([1, 0, 0, 1, 0, 0]),\n",
" n_samples=1000,\n",
")\n",
"\n",
"job = qpu.run(sampler)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once complete, results can be retrieved from the system & plotted."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"job.wait_until_complete()\n",
"\n",
"results = job.get_result()\n",
"results.plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
![](\"_static/hp_token_light.png\")
\n",
"![](\"_static/hp_token_dark.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Job submission\n",
"Once created, this token can then be used the same way as any other token would be, using the token management system to set, save & load as required."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import lightworks as lw\n",
"from lightworks import remote\n",
"\n",
"remote.token.set(\"HIGH_PRIORITY_TOKEN\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A basic sampling job can then be created and submitted. If there are other jobs in the queue when the job is submitted then these will be bypassed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"qpu = remote.QPU(\"Artemis\")\n",
"\n",
"sampler = lw.Sampler(\n",
" circuit=lw.Unitary(lw.random_unitary(6)),\n",
" input_state=lw.State([1, 0, 0, 1, 0, 0]),\n",
" n_samples=1000,\n",
")\n",
"\n",
"job = qpu.run(sampler)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once complete, results can be retrieved from the system & plotted."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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