diff --git a/docs/start/hello-world.ipynb b/docs/start/hello-world.ipynb
index 2f33b38992e..dc34bc1c179 100644
--- a/docs/start/hello-world.ipynb
+++ b/docs/start/hello-world.ipynb
@@ -23,7 +23,6 @@
"\n",
"It is recommended that you use the [Jupyter](https://jupyter.org/install) development environment to interact with quantum computers. Be sure to install the recommended extra visualization support (`'qiskit[visualization]'`). You'll also need the `matplotlib` package for the second part of this example.\n",
"\n",
- "\n",
"To learn about quantum computing in general, visit the [Basics of quantum information course](https://learning.quantum.ibm.com/course/basics-of-quantum-information) in IBM Quantum Learning.\n",
"\n",
"The four steps to writing a quantum program using Qiskit Patterns are:\n",
@@ -79,9 +78,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": [
- " "
- ],
+ "image/png": "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",
"text/plain": [
""
]
@@ -154,11 +151,9 @@
"metadata": {},
"source": [
"\n",
- "\n",
"Here, something like the `ZZ` operator is a shorthand for the tensor product $Z\\otimes Z$, which means measuring Z on qubit 0 and Z on qubit 1 together, and obtaining information about the correlation between qubit 0 and qubit 1. Expectation values like this are also typically written as $\\langle Z_0 Z_1 \\rangle$.\n",
"\n",
"If the state is entangled, then the measurement of $\\langle Z_0 Z_1 \\rangle$ should be 1.\n",
- "\n",
" "
]
},
@@ -182,11 +177,9 @@
"outputs": [
{
"data": {
- "image/svg+xml": [
- " "
- ],
+ "image/png": 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",
"text/plain": [
- ""
+ ""
]
},
"execution_count": 3,
@@ -232,7 +225,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- ">>> Job ID: cs29td975q40008tpph0\n"
+ ">>> Job ID: csbmgsbd3kwg008hdtp0\n"
]
}
],
@@ -303,39 +296,37 @@
"metadata": {},
"source": [
"\n",
+ " When you run your quantum program on a real device, your workload must wait in a queue before it runs. To save time, you can instead use the following code to run this small workload on the [`fake_provider`](../api/qiskit-ibm-runtime/fake_provider) with the Qiskit Runtime local testing mode. Note that this is only possible for a small circuit. When you scale up in the next section, you will need to use a real device.\n",
"\n",
- "When you run your quantum program on a real device, your workload must wait in a queue before it runs. To save time, you can instead use the following code to run this small workload on the [`fake_provider`](../api/qiskit-ibm-runtime/fake_provider) with the Qiskit Runtime local testing mode. Note that this is only possible for a small circuit. When you scale up in the next section, you will need to use a real device.\n",
+ " ```python\n",
"\n",
- "```python\n",
- "# Use the following code instead if you want to run on a simulator:\n",
+ " # Use the following code instead if you want to run on a simulator:\n",
"\n",
+ " from qiskit_ibm_runtime.fake_provider import FakeAlmadenV2\n",
+ " backend = FakeAlmadenV2()\n",
+ " estimator = Estimator(backend)\n",
"\n",
- "from qiskit_ibm_runtime.fake_provider import FakeAlmadenV2\n",
- "backend = FakeAlmadenV2()\n",
+ " # Convert to an ISA circuit and layout-mapped observables.\n",
"\n",
- "estimator = Estimator(backend)\n",
+ " pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
+ " isa_circuit = pm.run(qc)\n",
+ " mapped_observables = [\n",
+ " observable.apply_layout(isa_circuit.layout) for observable in observables\n",
+ " ]\n",
"\n",
+ " job = estimator.run([(isa_circuit, mapped_observables)])\n",
+ " result = job.result()\n",
"\n",
- "# Convert to an ISA circuit and layout-mapped observables.\n",
- "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
- "isa_circuit = pm.run(qc)\n",
+ " # This is the result of the entire submission. You submitted one Pub,\n",
+ " # so this contains one inner result (and some metadata of its own).\n",
"\n",
- "mapped_observables = [\n",
- " observable.apply_layout(isa_circuit.layout) for observable in observables\n",
- "]\n",
- "\n",
- "job = estimator.run([(isa_circuit, mapped_observables)])\n",
- "result = job.result()\n",
+ " job_result = job.result()\n",
"\n",
+ " # This is the result from our single pub, which had five observables,\n",
+ " # so contains information on all five.\n",
"\n",
- "# This is the result of the entire submission. You submitted one Pub,\n",
- "# so this contains one inner result (and some metadata of its own).\n",
- "job_result = job.result()\n",
- "\n",
- "# This is the result from our single pub, which had five observables,\n",
- "# so contains information on all five.\n",
- "pub_result = job.result()[0]\n",
- "```\n",
+ " pub_result = job.result()[0]\n",
+ " ```\n",
" "
]
},
@@ -361,9 +352,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": [
- " "
- ],
+ "image/png": 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",
"text/plain": [
""
]
@@ -398,7 +387,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "b8d8e086",
"metadata": {
"tags": [
@@ -434,7 +423,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "2ac02692",
"metadata": {},
"outputs": [],
@@ -475,7 +464,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "863a4ec9",
"metadata": {},
"outputs": [
@@ -511,7 +500,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 14,
"id": "428f05e7",
"metadata": {},
"outputs": [],
@@ -542,18 +531,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 16,
"id": "3aaa5025",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "cs29tpabqt7g0081ybs0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from qiskit_ibm_runtime import EstimatorOptions\n",
"from qiskit_ibm_runtime import EstimatorV2 as Estimator\n",
@@ -565,8 +546,24 @@
"options.dynamical_decoupling.sequence_type = \"XY4\"\n",
"\n",
"# Create an Estimator object\n",
- "estimator = Estimator(backend, options=options)\n",
- "\n",
+ "estimator = Estimator(backend, options=options)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "b4c3d3e7-0a0f-4023-8948-1082e225f46c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cs29tpabqt7g0081ybs0\n"
+ ]
+ }
+ ],
+ "source": [
"# Submit the circuit to Estimator\n",
"job = estimator.run([(isa_circuit, isa_operators_list)])\n",
"job_id = job.job_id()\n",
@@ -585,15 +582,13 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 19,
"id": "de91ebd0",
"metadata": {},
"outputs": [
{
"data": {
- "image/svg+xml": [
- " "
- ],
+ "image/png": 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",
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""
]