Repository: qiskit-community/povm-toolbox

Issue:
Implement dilation POVMs

Discussion:

Patch:
diff --git a/docs/how_tos/dilation_measurements.ipynb b/docs/how_tos/dilation_measurements.ipynb
new file mode 100644
index 0000000..c26b713
--- /dev/null
+++ b/docs/how_tos/dilation_measurements.ipynb
@@ -0,0 +1,911 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dilation POVMs and how to deal with ancilla qubits\n",
+    "\n",
+    "This document shows you how to use dilation measurements. More specifically, it showcases how we deal with required measurement ancilla qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## First example of a dilation POVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from povm_toolbox.library import DilationMeasurements\n",
+    "\n",
+    "# parameters that define a SIC-POVM (SIC stands for \"symmetric and informationally-complete\")\n",
+    "sic_parameters = np.array(\n",
+    "    [0.75, 0.30408673, 0.375, 0.40678524, 0.32509973, 0.25000035, 0.49999321, 0.83333313],\n",
+    ")\n",
+    "\n",
+    "povm = DilationMeasurements(num_qubits=1, parameters=sic_parameters, insert_barriers=True)\n",
+    "povm.definition().draw_bloch()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IC dilation measurements are defined on a space spanning (at least) four states: e.g. $\\{|0\\rangle,|1\\rangle,|2\\rangle,|3\\rangle\\}$.\n",
+    "To achieve such a measurement using qubits, every qubit gets paired with an ancilla qubit.\n",
+    "Then, the dilation measurement can be constructed via some two-qubit unitary followed by measurements in the computational basis.\n",
+    "The binary outcomes $\\{|00\\rangle,|01\\rangle,|10\\rangle,|11\\rangle\\}$ can then be mapped to the four states above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 961.687x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit import QuantumCircuit\n",
+    "\n",
+    "qc = QuantumCircuit(1)\n",
+    "qc.h(0)\n",
+    "\n",
+    "povm.compose_circuits(qc).draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding necessary ancilla qubits\n",
+    "\n",
+    "The user doesn't have to deal with adding ancilla qubits, the method `DilationMeasurements.compose_circuit` takes care of adding ancilla qubits when necessary. It will check if the input circuit has idling qubits that can be used. If there are not enough idling qubits in the input circuit, the method will add additional ancilla qubits to the input circuit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Quantum Circuit without ancilla qubits\n",
+    "\n",
+    "Example of a quantum circuit without idling qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 287.294x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(2)\n",
+    "qc.h(0)\n",
+    "qc.cx(0, 1)\n",
+    "\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1128.91x451.5 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "povm = DilationMeasurements(num_qubits=2, parameters=sic_parameters)\n",
+    "\n",
+    "# The input circuit does not have any idling qubits. The method `compose_circuit` will add two ancilla qubits.\n",
+    "povm.compose_circuits(qc).draw(\"mpl\", wire_order=[0, 2, 1, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Quantum Circuit with a few idling qubits\n",
+    "\n",
+    "Example of a quantum circuit with a few idling qubits, but not enough."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 287.496x284.278 with 1 Axes>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(3)\n",
+    "qc.h(0)\n",
+    "qc.cx(0, 1)\n",
+    "\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1128.91x451.5 with 1 Axes>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "povm.measurement_layout = [0, 1]\n",
+    "\n",
+    "# The input circuit has one idling qubit. The method `compose_circuit` will use it and also add one ancilla qubit.\n",
+    "povm.compose_circuits(qc).draw(\"mpl\", wire_order=[0, 2, 1, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Quantum Circuit with enough idling qubits\n",
+    "\n",
+    "Example of a quantum circuit with enough idling qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 287.496x367.889 with 1 Axes>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(4)\n",
+    "qc.h(1)\n",
+    "qc.cx(1, 3)\n",
+    "\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1128.91x451.5 with 1 Axes>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "povm.measurement_layout = [1, 3]\n",
+    "\n",
+    "# The input circuit has two idling qubit. The method `compose_circuit` will use both of them and does not need to add any additional ancilla qubits.\n",
+    "povm.compose_circuits(qc).draw(\"mpl\", wire_order=[1, 0, 3, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Quantum Circuit with many idling qubits\n",
+    "\n",
+    "Example of a quantum circuit with more than enough idling qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 287.496x535.111 with 1 Axes>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(6)\n",
+    "qc.h(1)\n",
+    "qc.cx(1, 3)\n",
+    "\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1128.91x618.722 with 1 Axes>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "povm.measurement_layout = [1, 3]\n",
+    "\n",
+    "# The input circuit has four idling qubit. The method `compose_circuit` will use two of them (and does not need to add any additional ancilla qubits).\n",
+    "povm.compose_circuits(qc).draw(\"mpl\", wire_order=[1, 0, 3, 2, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Quantum circuit with ancilla qubits that are not idling\n",
+    "\n",
+    "Example of a quantum circuit that has an `AncillaRegister` with non-idle qubits. These cannot be used and, thus, we need additional ancilla qubits to ensure all dilation measurements can be performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 388.934x367.889 with 1 Axes>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from qiskit.circuit import AncillaRegister, QuantumRegister\n",
+    "\n",
+    "qc = QuantumCircuit(QuantumRegister(2), AncillaRegister(2))\n",
+    "qc.h(0)\n",
+    "qc.cx(0, 1)\n",
+    "qc.sx(2)\n",
+    "qc.cx(2, 1)\n",
+    "\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1296.13x535.111 with 1 Axes>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We only want to measure qubits 0 and 1 (qubit 2 is an ancilla qubit)\n",
+    "povm.measurement_layout = [0, 1]\n",
+    "\n",
+    "# The input circuit has one idling qubit. The method `compose_circuit` will use it and also add one ancilla qubit.\n",
+    "povm.compose_circuits(qc).draw(\"mpl\", wire_order=[0, 3, 1, 4, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transpiled Quantum Circuits\n",
+    "\n",
+    "Examples with transpiled quantum circuits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate a generic backend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.providers.fake_provider import GenericBackendV2\n",
+    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
+    "\n",
+    "# Generate a 6-qubit simulated backend\n",
+    "backend = GenericBackendV2(\n",
+    "    num_qubits=6, coupling_map=[[0, 2], [1, 2], [2, 3], [4, 3], [5, 3]], seed=0\n",
+    ")\n",
+    "\n",
+    "backend.set_options(seed_simulator=25)\n",
+    "pm = generate_preset_pass_manager(optimization_level=2, backend=backend, seed_transpiler=0)\n",
+    "\n",
+    "coupling_map = backend.coupling_map\n",
+    "# coupling_map.draw()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define quantum circuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 287.294x200.667 with 1 Axes>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc = QuantumCircuit(2)\n",
+    "qc.h(0)\n",
+    "qc.cx(0, 1)\n",
+    "qc.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transpile circuit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1001.57x535.111 with 1 Axes>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Transpile the circuit to an \"Instruction Set Architecture\" (ISA) circuit.\n",
+    "# Note: the transpiler automatically adds \"ancilla\" qubits to make the transpiled\n",
+    "# circuit match the size of the backend.\n",
+    "qc_isa = pm.run(qc)\n",
+    "qc_isa.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define measurement procedure\n",
+    "\n",
+    "There is no need to set a particular `measurement_layout`, because the `POVMSampler` will analyze the transpiled circuit and apply the `TranspileLayout` to the measurement circuit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "measurement = DilationMeasurements(\n",
+    "    num_qubits=2,\n",
+    "    parameters=sic_parameters,\n",
+    "    insert_barriers=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More precisely, the measurement layout that will be automatically applied by the `POVMSampler` is extracted as :\n",
+    "```python\n",
+    "transpile_layout = transpiled_circuit.layout\n",
+    "measurement_layout = transpile_layout.final_index_layout(filter_ancillas=True)\n",
+    "```\n",
+    "For the above circuit, we have:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(qc_isa.layout.final_index_layout(filter_ancillas=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the job\n",
+    "\n",
+    "Initialize `Sampler` and `POVMSampler`. Then run the job. A pass manager has to be provided to transform the final composed circuit into an ISA circuit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from povm_toolbox.sampler import POVMSampler\n",
+    "from qiskit_ibm_runtime import SamplerV2 as RuntimeSampler\n",
+    "\n",
+    "# First define a standard sampler (that will be used under the hood).\n",
+    "runtime_sampler = RuntimeSampler(mode=backend)\n",
+    "# Then define the POVM sampler, which takes the sampler as an argument.\n",
+    "povm_sampler = POVMSampler(runtime_sampler)\n",
+    "# Submit the job by specifying which POVM to use, which circuit(s) to measure and the shot budget.\n",
+    "# A pass manager has to be provided to make the measurement circuit ISA.\n",
+    "job = povm_sampler.run([qc_isa], shots=4096, povm=measurement, pass_manager=pm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the final composed circuit to check that the measurement was performed on the correct qubits.\n",
+    "It is important to note that the two-qubit unitary which is part of the dilation measurement can cause a significant SWAP overhead, depending on the qubit connectivity between the measured qubit and its associated ancilla."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3135.58x618.722 with 1 Axes>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pub_result = job.result()[0]\n",
+    "pub_result.metadata.composed_circuit.draw(\"mpl\", fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define observable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qiskit.quantum_info import SparsePauliOp\n",
+    "\n",
+    "observable = SparsePauliOp([\"XI\", \"XX\", \"YY\", \"ZX\"], coeffs=[1, 1, -1, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the expected value\n",
+    "\n",
+    "The observable has to be specified in terms of virtual qubits. Therefore, there is no need to apply the layout that was used by the POVM on the physical qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0032563568087483\n"
+     ]
+    }
+   ],
+   "source": [
+    "from povm_toolbox.post_processor import POVMPostProcessor\n",
+    "\n",
+    "post_processor = POVMPostProcessor(pub_result)\n",
+    "exp_value, std = post_processor.get_expectation_value(observable)\n",
+    "print(exp_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For reference, we can compare our estimated expectation value to the exact value.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exact value:     1.9999999999999987\n",
+      "Estimated value: 2.0032563568087483\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qiskit.quantum_info import Statevector\n",
+    "\n",
+    "isa_observable = observable.apply_layout(layout=qc_isa.layout, num_qubits=qc_isa.num_qubits)\n",
+    "\n",
+    "exact_expectation_value = np.real_if_close(Statevector(qc_isa).expectation_value(isa_observable))\n",
+    "print(f\"Exact value:     {exact_expectation_value}\")\n",
+    "print(f\"Estimated value: {exp_value}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transpiled Quantum Circuit with ancilla qubits\n",
+    "\n",
+    "Example where the quantum circuit had ancilla qubits before transpilation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define quantum circuit\n",
+    "The circuit can contain some ancilla qubits that you might not want to measure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 287.496x367.889 with 1 Axes>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qc_with_ancilla = QuantumCircuit(4)\n",
+    "\n",
+    "# Qubits (1,3):\n",
+    "qc_with_ancilla.h(1)\n",
+    "qc_with_ancilla.cx(1, 3)\n",
+    "\n",
+    "# Ancilla qubits (0,2):\n",
+    "qc_with_ancilla.z(0)\n",
+    "qc_with_ancilla.z(2)\n",
+    "\n",
+    "qc_with_ancilla.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Transpile circuit with ancilla qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1001.37x535.111 with 1 Axes>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Transpile the circuit to an \"Instruction Set Architecture\" (ISA) circuit\n",
+    "qc_with_ancilla_isa = pm.run(qc_with_ancilla)\n",
+    "qc_with_ancilla_isa.draw(\"mpl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define measurement procedure\n",
+    "\n",
+    "The `measurement_layout` argument specifies which qubits to measure. This argument overrides the automatic detection of a possible `TranspileLayout` attribute of the supplied circuit. I.e., it specifies directly the (final) physical qubits on which the POVM will act. In this example, we want to measure physical qubits 4 and 3.\n",
+    "\n",
+    "If you want to specify the virtual qubits instead (here virtual qubits 1 and 3), you have to do the composition with the `TranspileLayout` manually as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final measurement layout: [2, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Measuring virtual qubits 1 and 3\n",
+    "virtual_msmt_layout = [1, 3]\n",
+    "# Get the transpilation layout\n",
+    "transpile_layout = qc_with_ancilla_isa.layout.final_index_layout()\n",
+    "# Compute the final measurement layout\n",
+    "final_msmt_layout = [transpile_layout[idx] for idx in virtual_msmt_layout]\n",
+    "print(\"Final measurement layout:\", final_msmt_layout)\n",
+    "\n",
+    "measurement = DilationMeasurements(\n",
+    "    2,\n",
+    "    measurement_layout=final_msmt_layout,\n",
+    "    parameters=sic_parameters,\n",
+    "    insert_barriers=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the job\n",
+    "\n",
+    "Initialize `Sampler` and `POVMSampler`. Then run the job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First define a standard sampler (that will be used under the hood).\n",
+    "runtime_sampler = RuntimeSampler(mode=backend)\n",
+    "# Then define the POVM sampler, which takes the sampler as an argument.\n",
+    "povm_sampler = POVMSampler(runtime_sampler)\n",
+    "# Submit the job by specifying which POVM to use, which circuit(s) to measure and the shot budget.\n",
+    "job = povm_sampler.run([qc_with_ancilla_isa], shots=4096, povm=measurement, pass_manager=pm)\n",
+    "pub_result = job.result()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check that measurement was performed on the correct physical qubits by looking at the final composed circuit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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AU121JQfMLjElSydO5Rl2/E2x9AGjGX0eMvr4VVVb2r8ijIcurDbnAO3vmNqWA3a7Xe+++66ys7MlSX369NHkyZPl6+tb5f3NnTtXK1askFRcFObj4yNJ+vbbbxUba8y0xAAujEIwAKUsWLBANptN48aNU506dcpdJygoSFLpQrCXX35ZPj4+eu6557R8+XJNmjRJb7/9toYOHVqmStxZAwcOVOvWrS/4p23btlq9unj+8vj4eI0fP75axz1famqqhg4dqpMnT+rqq6/Wyy+/7LJ9Gynipickq7XU3Q6Z239Q0vy/q82U/ymgSZRxwVVR1MhLNeSjx9Wkf3Sp1/3qBqsgM7vC7ba9tkgndx5Sn6duV/9/36XwXu21+cUFOn0g2d0hG6Y2tj9Qns2xaVWaquJgUqY27DzuhogAADXFvn37NHjwYO3bt8/oUAxRG8eDXA84hxw4hxzw3hzYG3/K4ONfePr5mqw25IDZGZ2DRh+/umpDHzC6DYw+fnXUhvYvD+Mhx9XGHKD9nVObcmDdunXasmWLJKl+/fq6//77Swq3nFVeEdikSZN06623liyfM2dOyfSTAGoWi90bJ+8G4DaXXXaZfvnlF33xxRe6/vrry13nhhtu0JdffqklS5boxhtvlCQdP35c4eHhpdb75JNPNG7cOP30009Vmid60KBB+umnn9S1a1f5+/s7tM2JEydKpoW89957NWfOHKeP+0cnT57UoEGDtGPHDg0YMEArVqxwag7tP+rTp49SU1Od2sbiH6QmM93/5VTe0Xjt+VtfRd7ytBqPeKBa+zr6cHvZ83NcFJnkZ7fqaVs/h9bt9tAoBTSsqw1Pz5MkhTQLU6vh/bX73WWVbtegcytdu/wF+fj76ejvsVp+41NSFd8mp1vXq8DiurtkPJEDrmx/yfU5AFRVrl97nQitWnFww8xPFFTAFJEA4E3GjBnj8LrHjh3TggULdOutt6px48YOb7do0aKqhFYtZh8Petv1gMQ1gdmvCSXX5gCfC1QsM/BSnQ6+ptxlGxaMVERY5Z/hRIQFydfHqsIim1LTKo43NS1bfW8tOy1ScO5GNcj+yrmgneSp9pdq9nsBypfj11EnQ28rd5kn+oB/wUGFZ37oXNBVwHmwYhlBVysraEC5yy6UA462v1RxDoTm/KC6OT86FXNVeNtYSDJuPCzVjPGQ2cfDEtdEjIdd+z7g7+9f4WxOdrtdU6dOLfmO9G9/+5v69OlTpeNUVAR2+eWXy2az6emnny65oe6RRx5Rv34V5+XUqVOVn59fpTjgXjfe+bBC6tTVmazT+nzuzApfg3EiIiK0cePGKm1btecAAqi1EhISJEmtWrUqd3lhYaF++eUXSaWfCPbHIjBJJQOMI0eOVCumpUuXKioq6oLrJSUl6YorrpAkjR07VrNnz67WcSXp9OnTGjp0qHbs2KFevXpp2bJl1SoCk4qfLubs/4k1IFhNqnXUC7PlZevA8zeoXr+RLrnASU5Oli2v4rtLnOVv8ZGj/wkJy9frynl/L7nIaXFVHx1eceE3yoLT2bLlF8rH309JqzZX6wInOSVZ+XbXTTHk7hxwdftLrs8BoMrq1JNCq7bpybRjUlb13scAAJ515swZh9fNyckp+duZ7ap7jVMVZh8Petv1gMQ1gdmvCSXX5gCfC1Si0Smpgo9qIsKC1bxJiEO78fWxOrzu+bLPZCo72b3vC55of6nmvxegAqFhFV7zeqIP5OfmemRsxHmwEhGZUlAFixzMgaq2vyRlns5Q5jHvz4Gafg50Ziwk1YzxkNnHwxLXRIyHXfs+EBAQUOGy/fv3lxSBtW3b1i1FYJJktVp100036bnnnpMkrVixotJCsOTkZOXlGTeFNSpmKyoq+fvsWK681+CdKAQDUMrZLz/OfiHyRwsXLlRaWppCQ0PVunXrSvf1ww8/SJKio6MrXc8Vjhw5opiYGB08eFBjxozRxx9/XOXHnZ6VnZ2ta6+9Vhs2bFB0dLS+++471atXr9qxRkREOL2Nxb+CK3kXSl+3WDmHtin3SJzS1y4ss7zLm7vlH97S4f01bdrU5Xd/y8EbRzLikiS7VL9Dc52KS1Jo6whlzjt6we0GzPyzrH6+OhV3WN0eHq34peuUmXDh7crTNLKpy+92cidXt7/k+hwAqqrQatVRqfiDC4vFsY3+f90mDSzyrdfMneEBAFwsJMTxL7DOXv8EBQU5tV2zZp5/bzD7eNDbrgckrgnMfk0ouTYH+FygYmf8A3SqgmWpaRf+4s2ZpyGVp06wn+q5+X3BE+0v1fz3ApQvzzdEaRUs80QfCAyQGnlgbMR5sGKZgX46XcGyC+WAs08EK0+9OgGq4+f9OVDTz4HOjIWkmjEeMvt4WOKaiPGwa98HKps96ex3spJ01VVXVWn/FyoCO+uiiy5SRESEUlNTtXPnTh07dqzCp6w3bdqUJ4LVUNb//x7d6uNT8jlXea/BOFWpKTiLQjAApURERCg9PV2bN2/WJZdcUmpZSkqKHnvsMUlSt27dZKnky/QjR47on//8p4YOHaoePXq4M2RJ0vTp07V//37deOONWrBggXx9q3d6y8/P14033qg1a9aobdu2WrlypcLCwlwSa1Ue4ZhTKA38xiWHr1CjmAlqFDPBZfuLi9unIBe+yxRk5+q/bR2f2u3w9xvV4pq+ykpKU0HWhQfa0XcPV+SAi7Tp+U90+Nv1um7FDA14bbK+HfV0leKN2xcnv+DAKm1bHnfngKvbX3J9DgDVcdW9y7Xyt2THN7BYFNM3Uqvf3+6+oAAAbrFhwwaH192zZ48WLFigYcOGqVOnTg5vN3PmzCpEVj1mHw962/WAxDWB2a8JJdfmAJ8LVGzjruPlTlUmqcLXz3f4+1vUvEmIUtNy1OKqT50+/pyZT+q2EXOd3s4Znmh/qea/F6B86afz1PCyj8td5ok+MPXh8Xrq/lec3s5ZnAcr9tWPiRr54PflLrtQDlS3/SXp8wVvKKZf0ypt6wxvGwtJxo6HJePHQ2YfD0tcEzEedu37QGFhoRYvXlzusrNTNfr4+JT5ftcRjhaBScVPBRswYEBJLPv376+wECwuLq7a39nCPZ576786nXVGkRGRSkpKqvA1eCer0QEAqFmGDBkiSXrxxRcVFxdX8vqGDRsUExOjtLTi+8sqK+7KysrS9ddfL39/f33wwQdVjuWKK67Q6NGjHboz//XXX9f06dO1cOHCag8oioqKdNttt2nFihVq3ry5Vq5cqaZN3X8hC9c6vGKjWlzdR00HdVfyz5UXcoS2jlCvx2/T8S37tPPNL3QqLklbX/lMEZd0UfTdwz0UMQB3mjzW+adTVmUbAIB3adeunb777ju1a9fO6FDgYlwPgBwwj67tG8rP17iPuft0cc2Ng0BVNagboDbNK5gb0gPoA8br3bmRocfvFU0O1FSMh8yN9jePvLy8kqKd5s2bVzqFZHmcKQI7q23btiX/PnToUBWiBuBOFIIBKGXKlClq1KiRDh8+rC5duqhr165q3769+vXrpzZt2mjw4MGSpO7du5e7fU5Ojq677jodOnRIK1asUGRkZJVjmT59uhYtWqTw8PALrhsUFKSnnnpKfn5+VT7eWZ999llJFbu/v7/Gjx+vyy67rNw/qamp1T4e3OPo77Gq2yZSrYb10/ENeyte0WLRZTMfkNVq1dqH3pTdVvyY4p1vfam0rfvV6/HbFNrK3TPPA3C3Gwa30u3XOf4l/7gRbTX6qij3BQQAqBF8fX3VoEED7k6thbgeADlgHgH+PuoVbUwRRKP6AWrXsq4hxwbOd0n38p/C4W5Wq0V9KQQzXNPGIWoZ6fg0567UpW191QuteKoyGIvxkLnR/uaRlJQku90uSWrdurVT21alCEyS2rRpU/LvhIQEJyMG4G4UggEopXnz5lqzZo1GjBihwMBAxcfHq2HDhpozZ46WLVtW8pSw8grBCgoKNGbMGG3cuFHLly9X586dPR2+S+Tl5ZX8++DBg/rll18q/JObm2tgpKiMvcimIz9uK/63reI56Lvcf52a9OukLTMWKmPfkXPb22xa+9Cbsvr4aMBrk90eLwD3slgsem/aQN11Y4cLrnvHyPb64JmBlU6BDACoHZKSkvToo4/yuPtaiOsBkAPmcrcD43x3uOuGDrJauW6A8YzqA9fHtFR4wyBDjo3S7r6xozHHHWXMceEYxkPmRvubR07Ouak/69Wr5/B2VS0Ck6S6dc/dDHH+8QHUDBSCASgjOjpaX3/9tTIzM5WZmanff/9d9957r86cOaP4+HhZrVZddNFFpbax2WwaN26cVq1apS+//FL9+vUzKPrqmzhxoux2u0N/oqKijA4XlUhcvl6J326ocHm99s3Ua8otOrZxr3b956syy3n8MVC7+PlZ9d60y7T2w2t12/C2ZaaPuXVYG/08d4Tm/mug/P18DIoSAOBJWVlZWrNmjbKysowOBW7A9QDIAfO4bXhbjz+RxmKR7r+5k0ePCVRkUN9IRbep7/HjTh4b7fFjonz3jO4oX1/PFqYGBfpo4vXtPXpMOI/xkLnR/ubQsWNHzZ49WzNnztSIESMc3i4/P79kWkdnisAkyWq16tVXX9WsWbP02GOPVSluAO7D3AcAHLZr1y7Z7XZ16NBBwcHBpZb9+c9/1v/+9z/94x//UHBwsH777beSZW3btnVoekfA1RK+/rXS5Rn7juij1rdVus6OWZ9rx6zPXRkWAANZLBYN6NlEA3o20ZynBqjd8M909GSuIsOC9MmLMUaHBwAAXIjrAZAD5hES7KfJN0fr+fe3eeyYo4dEqU1zpoVEzWCxWPS3O7rq7qfXeOyYPTo11OB+TT12PFQuMjxY40e007wv93nsmH8a1VEN6gZ47HioGsZD5kb7m4Ofn58aNmzo9HYBAQGaOnWqXnzxRV155ZUOF4Gd1bQp4wCgpqIQDIDDduzYIan8aSGXL18uSXrhhRf0wgsvlFo2d+5cTZw40e3xoWrSf1mk09tXqcXdr+ngy7co9/BuWf2D5FuvsVpOeluBke2MDhFuRg7ArOoE+8n3/58KxnQuAAAAgHf75309tGRVvPbGZ7j9WA3q+uuNf1zi9uMAzrjzhvb65JsDWvV7stuP5etr0dxnLq9x19K5yfsUP/MOFWamySe4nqIemqegll1KrXN6+2odmf8P2XKyJItF9fqMULPbX5DFWvz5QOriF3Vi9Yey+PnL6heoFve8oZAOxbNfnPjhIx394mXZbUXyq99EUQ/OlX94S4//nhV5+dF+Wr42SUdPuH+KrlZN6+jff+nt9uNUR+I7Dypjw1LlH0tQ9GtbFNymR5l17Dabjnw4RRmbv5W9qFB1ogeo5f1vy+rnr4zN3+nI/L+XrFt46ph8G0So82ubPfhbAIB7BQcH6+mnn5bVykRyQG1CIRgAh1VWCBYfH+/haOAqp377XA1jbpckhV99r+r2HiaLxaJjy95Uwpt/Usd//2hsgHA7cgAAAMB8HPlirPD0CcU9dWXJz7a8bOWlHlT3+cfkG+r83cYwji0/1+mbPtJWzlXCrLvUdurnqn/xDSWvO5I7qJninr5ahempktUqn6BQtbjnDQW36VlmvcoKKaqSS54SFOiruf8aqMvuWCabze7wdqlp2aX+dsSsqZcoMjz4wisazJGiGEnacU+ULL4BsgYESZIiRk9Vw4FjJVXe5jU5H8zIYrHo/emX6aJRnysru8Dh7arSB568p4d6dGrkdIzuljj7PoVdc6/Crpyo9F8WKf71iYp+pfSUaL51GqjN3z5VQEQb2fJzFffUEJ34Yb7Crpyo7INbdXz5bHWetUs+QXV04sePlfjOA4p+eb1yk/Yoad5j6vzaFvk1jNSJHz9WwtuT1P6pZQb9tmU1qh+oOU8N0A0PrXR4m6q0vyS9P22gQkM8OyWvsxoMGKOIUVO0d+plFa6TtvJ9ZR/YrOhXN8vi66fEt+7Vsa9eV8Sox1Sv1zWq1+uaknX3/+tahXblafLezBXXQBmbv1Xyx0/KXpgva0CwWk6eo+DWZb8zQ83jzLilsrFRbcwBisCA2odCMAAOq6wQDDVXYdYp7X7wItnyc+Qf1kL2gjzlpR5Uw0ET1GrS28ra84uiHponi6+f6vU5N8d7SIeLdfSLlw2MHK5CDgAAAJQvPDxcDz30kCmnsnfkizHfuo3UeebWkp9TP39ZWbt+ogjMSzlz00fe0XilrXhXIR0vLrPMkdxBzdTmsc/kW6e+JCn9188V//pEdX697FSKFyqkqMk3EF3SvYnenHqJJv97ncPb9L11qVPHeHh8F902vK2zoRnCkaKYs9o8trDCws7K2rwm54MZtWoaqk9fGqQbHl6pwkLHCiKd7QM3DG6lJ+7pUYXo3Kvg1DGd2b9R7aevkCTVv3S0Et95QLkp+0t9yX9+AazVP1DBrXso/1h88QsWi+yFBbLlnZFPUB0VnTkl/0bNJUk5CTsVFNVNfg0jJUn1eg9X/MzbVXj6hHzr1pyiuOtjWumf9/XQv+ZsdWh9Z9tfkl58uK+uvLjmTwcW2uXCU5zlHNqm0O5DZPUrLmqr23uYUhZMU8Sox0qtl38iWae3r1Krv3zglljhGdW9BirMStehV8ep43M/K6hlF2XuWqNDr45Tl1k7PRA9XMGZcUt5YyNyAIC3oLwTgMNWr14tu92uESNGGB0KnOBbp74aXn6bmlz3sDrP3Krmd89USMeLFfWX95S54weFdLpUFl+/Mtsd+/p11e93vQERw9XIAQAAgPI1atRI48aNU6NGNefLO08J7XK5/MOaO7XNiZXvK2zI3W6KCO5k9Q9UvT7DZbEUT+EV0uHic196/4HdZlPCm39Si3tnyeIXUGZ5VXIHNcPZIjBJKsrOkCxlp3Q7W0jRaNB4ScWFFPlph5Wbsl+Sc7lklEljo/XaY/3dsu8Hbu2sV/7Wv+T3r8ku1JaOqqzNvSEfzGjE5S312YzB8vdz/dc/Iwe11IIXB8nXt+Z9tZSfdlh+DSJl8Sl+/oHFYpF/eEvlH0+scJuC9FSlr1uken2ulSQFt+6uxiP/qh33tNb2u5rr6NLX1OLeWZKkoNbdlX1gs3KPxEmSTv74sWS3K/94gpt/M+dNn9xL/7i7m1v2/ewDvTXlLvfs2wjBbXsrY/1SFWWflr2wQOlrP1NeOeexE6vnqV7v4fKr39jzQcJlqnsNlJdyQL6hjUqerhnaZaDyjycq+wDThXoDV4xbyAEA3qLmjdYBAC6XfWirgv7/brfsA5tK7nw79fsXanDxjWXWT/nfc8pL2a9mtz/v0TjhPuQAAABAWadPn9bKlSt1+vRpo0Op8bJi16kwK131+l5rdChwgcpu+jj65auqEz1AIe16ezgqeMKh127X9rtaKPm//1Trhz8qs9zZQoqaegPRwxMu0mcvD1bDemWLGasiMMBHrz3WX2/842JZrTW/CExyvi3jZ96uXQ92Vfysu1WQcbzC/VbW5jU1H8zoxiuj9P2coWrVtI5L9me1WvS3O7pq0StXKjCgdkw0U5R9WvufvU4Ro6YopH0fSVLe0UM69dsSXfSf/er2QZKajPyrDs4ongossGl7tZr0H8XPvF2xj/RRYeYJ+YTUl3xq3v+HxWLR8w/11X/+OUAhQa6Jr24dP3347OV64t4eLtlfTdHoyomq22uo9j5+hfY+foUCm3YoOW+eZbfbdWLlB9wQYUJ/vAYKbNpehZknlBVb/OTRU78vlS0ns9ziQdR8Fxq3lDc2IgcAeIuaN0IFALhczqGtJYU/2Qc2qX6/kbLb7crY8p2a3fFSqXVTP39Zp35dovbPrJQ1INiIcOEG5AAAAEBZycnJevzxxzV//nzVrVvX6HBcZs+US5SbvK/cZZ1f2yL/8BZO7zNt5ftqFHN7mS/GUDM40+Znb/po9a9VZdbNSdipU78uVsfnfnZbrHAPR3Og9V/nS5JOrP5QSfP/rvZPfVPlY1aWSzXBTVe31sBeTTTp2XX6YnXVn9hzSffGmvvMQHVsXd91wbnAhdrcGR2f+1n+4S1lLyzQkf8+qfjX7yg3Nypr85qeD2Z0eZ9I7Vh8o6a8ukH/+d+eKu+nY1Q9zf3XQF3SvYkLo3M9/7AWKkhPkb2oUBYfX9ntduUfT5R/eMsy6xZlZ2rftKGq3/96Nbn+kZLX09ctVlCrrvJvVDztYaMr79Thd/4iW0G+rH7+ajBgjBoMGCOp+GliqUteLDXtZE1z302ddPUlzXTX02v044aUKu9n6IDmeufpAWoR4ZrCwprEYrGo6a3T1PTWaZKkkz9/WvKkn7Oydv4kW0Gu6va8xoAI4ShPXAP5hNRT2ymLdOSjqbLlZimk4yUKbNFZFivXSDWBq66JpIrHRuQAAG/BWQkAarn8E0ckWeTfqJkkKSd+uyJvekLZcesV1DxaPkHnLuCPfvmq0tcsUPtnVpaaMgLejRwAAAAwl04v/erS/RXlZCl97WeKfmWDS/cL13G0zS9000fW7jXKOxavnZPaSyr+kjvh8L0qSE9R+LBJLo0ZruVsv280+A4lvH2/Ck+fkG/dc9PjOlpI4S03EEWEBWvJa1fq123H9PZnsfrsu0PKL7BdcDuLRRoxsIUmj43WNQOa18ingF2ozS1+AQ4XxZx9zeLrpybXPaydkzqUWaeyNveWfDCj0BB/vf3PAXrg1s56+7NYzf9qvzLPFDi07cBeTTR5bLRGDYmSv5+PmyOtPr/6jRXctpdO/Pixwq6cqFPrFsu/UfMyhVpFOVnaN32o6vYaqsibnyy1LCCijU6smquinCz5BNVRxoavFdC0g6x+/pKkgpMp8msYKXtRkZI+/LvCh/+5xud86+ahWvXuMK1en6zZC2P15Q+JstnsF9zO19eiUVdGafLYaF3eO8IrpsStClt+rmz5OfKt00CFp9OUuuQFNb3tX6XWSVv5vhoNniiLT83vB2bmqWug0G4x6tgtRpJkK8jT9jsiFNiys0uPjapx1TWRVPnYiBwA4A0oBAOAWi774JaSJ0FJkk9IfR37ZrZ864apfv8bSl7PT0tS0gePyj+ijeKeLB7EWnwDFP3y754OGS5GDgAAAKA60tcuVFDr7gps3snoUFANjtz0ET5sUqmCr71PDFKT6x5W/Ytv8EyQcJvCrFOy5WWXPOXm1G9fyDe0kXxCG5Zaz5FCCm+7gchisejSHk10aY8mevVv/bV6fYo27U7Tpt1pOnz0jPLyi+Tv56PGDQPVp3OYencO0xV9ItSqaajRoVeLw0UxuWdkLywoacuTaxaU+gxBqrzNvS0fzKpLuwZ68/FL9fxDfbTq92Rt2n1Cm3an6WBSpnLyCuXna1XDegHq2amRencO08BeEYpuU9/osJ3WatIcxb8xUamLnpNPUF1FPThXkhQ/60+q32+k6vcfqWNfva4z+9bLlndGp35bIklqcOlNirz5CdW/+EZl79ug2Ef7yOoXIGtAiFo/+knJ/uNn3aX8YwmyFeapXu8RajbhOUN+T2dZrRYNubiZhlzcTEeOntEPG86dB1NP5Cgvv0gB/j5qGh5cch6M6RepiLCaXeR2IQmz71PGxmUqSE/VvmnXyCcoVBfN2V8qH4qyMxT3xCDJYpXsNjW+9iHV73ddyT6KzmTo1K9L1PmNHcb9IjBERddAZwtCJSll4b8U2m1wjX4yIEpzZNxyobEROQDAG1AIBgC1XP2+16r+/89hL6nkDpZdD3RRk2d/KHndP6y5en954bvB4H3IAQAAAPxRRV+MSaW/LJWktO/fV9jV9xgZLqrpQjd9/LHNK1NZ7qDmKsrO0MGXbpItP0cWi1W+dcPV7smvS57wcn4OVFRIIXn/DUThDYM0dmgbjR3axuhQPKKytjzb5kGtLtKBF0ZLtiLZZVdAkzaKenh+yXqVtbm354MZhYb464bBUbphcJTRobhFYPOO5T4RJuov75X8O/LmJxR58xPlbm+xWNTs9ufV7Pbny13e/unlrgnUQM2ahGj8te00/traX7T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+      "text/plain": [
+       "<Figure size 3135.58x618.722 with 1 Axes>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pub_result.metadata.composed_circuit.draw(\"mpl\", fold=-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the expected value\n",
+    "\n",
+    "The observable has to be specified in terms of virtual qubits. Therefore, there is no need to apply the layout that was used by the POVM on the physical qubits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exact value:     1.9999999999999987\n",
+      "Estimated value: 2.0032563568087483\n"
+     ]
+    }
+   ],
+   "source": [
+    "post_processor = POVMPostProcessor(pub_result)\n",
+    "\n",
+    "print(f\"Exact value:     {exact_expectation_value}\")\n",
+    "\n",
+    "exp_value, std = post_processor.get_expectation_value(observable)\n",
+    "print(f\"Estimated value: {exp_value}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/docs/how_tos/index.rst b/docs/how_tos/index.rst
index 2c83617..19fdd12 100644
--- a/docs/how_tos/index.rst
+++ b/docs/how_tos/index.rst
@@ -8,4 +8,9 @@ This page will summarize the available how-to guides.
    :maxdepth: 1
    :glob:
 
-   *
+   visualization
+   layout
+   job_recovery
+   dual_optimizer
+   dilation_measurements
+
diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst
index 489863b..5887277 100644
--- a/docs/tutorials/index.rst
+++ b/docs/tutorials/index.rst
@@ -7,5 +7,8 @@ This page will summarize the available tutorials.
 .. nbgallery::
    :glob:
 
-   *
+   getting_started
+   povm_sampler_pub
+   parametrized_circuit
+   randomized_measurements
 
diff --git a/povm_toolbox/library/__init__.py b/povm_toolbox/library/__init__.py
index 6b03de4..e1f27ee 100644
--- a/povm_toolbox/library/__init__.py
+++ b/povm_toolbox/library/__init__.py
@@ -29,6 +29,7 @@ Below you find an overview of the implemented POVMs.
    LocallyBiasedClassicalShadows
    MutuallyUnbiasedBasesMeasurements
    RandomizedProjectiveMeasurements
+   DilationMeasurements
 
 Submodules
 ----------
@@ -41,6 +42,7 @@ Submodules
 """
 
 from .classical_shadows import ClassicalShadows
+from .dilation_measurement import DilationMeasurements
 from .locally_biased_classical_shadows import LocallyBiasedClassicalShadows
 from .mutually_unbiased_bases_measurements import MutuallyUnbiasedBasesMeasurements
 from .povm_implementation import POVMImplementation
@@ -52,4 +54,5 @@ __all__ = [
     "MutuallyUnbiasedBasesMeasurements",
     "LocallyBiasedClassicalShadows",
     "ClassicalShadows",
+    "DilationMeasurements",
 ]
diff --git a/povm_toolbox/library/dilation_measurement.py b/povm_toolbox/library/dilation_measurement.py
new file mode 100644
index 0000000..eeac6f8
--- /dev/null
+++ b/povm_toolbox/library/dilation_measurement.py
@@ -0,0 +1,315 @@
+# (C) Copyright IBM 2024.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""DilationMeasurements."""
+
+from __future__ import annotations
+
+import logging
+import sys
+import time
+
+if sys.version_info < (3, 12):
+    from typing_extensions import override
+else:
+    from typing import override  # pragma: no cover
+
+import numpy as np
+from qiskit.circuit import ClassicalRegister, QuantumCircuit, QuantumRegister
+from qiskit.primitives.containers import DataBin
+from qiskit.primitives.containers.bindings_array import BindingsArray
+from qiskit.primitives.containers.bit_array import BitArray
+from qiskit.primitives.containers.sampler_pub import SamplerPub
+from qiskit.transpiler import StagedPassManager
+
+from povm_toolbox.quantum_info import ProductPOVM, SingleQubitPOVM
+from povm_toolbox.utilities import gram_schmidt, n_sphere
+
+from .metadata import POVMMetadata
+from .povm_implementation import POVMImplementation
+
+LOGGER = logging.getLogger(__name__)
+
+
+class DilationMeasurements(POVMImplementation[POVMMetadata]):
+    r"""A measurement leveraging Naimark's dilation theorem.
+
+    IC dilation measurements are defined on a space spanning (at least) four states: e.g. :math:`\{
+    |0\rangle,|1\rangle,|2\rangle,|3\rangle\}`. To achieve such a measurement using qubits, every
+    qubit gets paired with an ancilla qubit. Then, the dilation measurement can be constructed via
+    some two-qubit unitary followed by measurements in the computational basis. The binary outcomes
+    :math:`\{|00\rangle,|01\rangle,|10\rangle,|11\rangle\}` can then be mapped to the four states
+    above.
+
+    There are 8 degrees of freedom in the two-qubit unitary specifying the dilation measurement (for
+    each qubit). Different parametrizations of the unitaries are possible. Here, we use the same
+    parametrization of the dilation POVM as the one presented in the work of G. García-Pérez, M. A.
+    Rossi, B. Sokolov, F. Tacchino, P. K. Barkoutsos, G. Mazzola, I. Tavernelli, and S. Maniscalco,
+    “*Learning to measure: adaptive informationally complete generalized measurements for quantum
+    algorithms*”, PRX Quantum 2, Publisher: American Physical Society, 040342 (2021). Refer to this
+    work for a detailed explanation of the parametrization.
+
+    .. note::
+        An additional ancilla qubit is required for each qubit in the system to be measured.
+        Depending on the qubit connectivity the coupling of the measured qubit with its ancilla can
+        introduce a significant overhead of SWAP gates.
+
+    The example below shows how you construct a dilation POVM. It plots a visual representation of
+    the POVM's definition to exemplify the different effects' directions.
+
+    .. plot::
+       :include-source:
+
+       >>> import numpy as np
+       >>> from povm_toolbox.library import DilationMeasurements
+       >>> povm = DilationMeasurements(
+       ...     1,
+       ...     parameters=np.array(
+       ...         [0.75, 0.30408673, 0.375, 0.40678524, 0.32509973, 0.25000035, 0.49999321, 0.83333313]
+       ...     ),
+       ... )
+       >>> print(povm)
+       DilationMeasurements(num_qubits=1, parameters=array([[0.75      , 0.30408673, 0.375     , 0.40678524, 0.32509973,
+               0.25000035, 0.49999321, 0.83333313]]))
+       >>> povm.definition().draw_bloch()
+       <Figure size 500x500 with 1 Axes>
+    """
+
+    def __init__(
+        self,
+        num_qubits: int,
+        parameters: np.ndarray,
+        *,
+        measurement_layout: list[int] | None = None,  # TODO: add | Layout
+        insert_barriers: bool = False,
+    ) -> None:
+        """Initialize a dilation POVM.
+
+        Args:
+            num_qubits: the number of qubits.
+            parameters: can be either 1D or 2D. If 1D, it should be of length 8 and contain float
+                values that specify the parametrization of the dilation POVM. If 2D, it will have
+                a new set of parameters for each qubit. The 8 values fix all the degrees of freedom.
+            measurement_layout: optional list of indices specifying on which qubits the POVM acts.
+                See :attr:`.measurement_layout` for more details.
+            insert_barriers: whether to insert a barrier between the composed circuits. This is not
+                done by default but can prove useful when visualizing the composed circuit.
+
+        Raises:
+            ValueError: if the last dimension of ``parameters`` is not of length 8.
+            ValueError: if the shape of ``parameters`` is not valid.
+        """
+        super().__init__(
+            num_qubits, measurement_layout=measurement_layout, insert_barriers=insert_barriers
+        )
+
+        if parameters.shape[-1] != 8:
+            raise ValueError(
+                "The last dimension of ``parameters`` is expected to be of length 8, but has"
+                f" length {parameters.shape[-1]} instead."
+            )
+
+        if parameters.ndim == 1:
+            parameters = np.tile(parameters, (self.num_qubits, 1))
+        elif (
+            parameters.ndim == 2 and parameters.shape[0] != self.num_qubits
+        ) or parameters.ndim != 2:
+            raise ValueError(
+                "``parameters`` is expected to have shape (8,) or (``num_qubits``, 8)"
+                f" but has shape {parameters.shape} instead."
+            )
+        self._parameters = parameters
+
+        # NOTE: this public attribute inherits its docstring from the base class
+        self.measurement_circuit = self._build_qc()
+
+    def __repr__(self) -> str:
+        """Return the string representation of a DilationMeasurements instance."""
+        return f"{self.__class__.__name__}(num_qubits={self.num_qubits}, parameters={self._parameters!r})"
+
+    @override
+    def definition(self) -> ProductPOVM:
+        t1 = time.time()
+        LOGGER.info("Building POVM definition")
+        sq_povms = []
+        for i in range(self.num_qubits):
+            unitary = self._unitary_from_parameters(self._parameters[i])
+            # In qiskit the order of qubits is reversed. Here we re-order it such that the
+            # unitary is defined on the Hilbert space `H_q \otimes H_a`` (where "q" stands
+            # for the system qubit and "a" for the ancilla qubit). In this way we can extract
+            # the correct POVM effects.
+            unitary[:, [1, 2]] = unitary[:, [2, 1]]
+            unitary[[1, 2]] = unitary[[2, 1]]
+            sq_povms.append(SingleQubitPOVM.from_vectors(unitary[:, 0::2].conj()))
+        prod = ProductPOVM.from_list(sq_povms)
+
+        t2 = time.time()
+        LOGGER.info(f"Finished POVM definition. Took {t2 - t1:.6f}s")
+
+        return prod
+
+    def _build_qc(self) -> QuantumCircuit:
+        """Build the quantum circuit that implements the measurement.
+
+        In the case of dilation measurements, the circuit is fixed (not parametrized as
+        for randomized measurements for instance). However, an additional ancilla qubit
+        is required for each qubit that we want to measure.
+
+        Returns:
+            Quantum circuit that implements the dilation POVM defined by :attr:`._parameters`.
+        """
+        t1 = time.time()
+        LOGGER.info("Building POVM circuit")
+
+        qr = QuantumRegister(2 * self.num_qubits, name="povm_qr")
+        cr = ClassicalRegister(2 * self.num_qubits, name=self.classical_register_name)
+        qc = QuantumCircuit(qr, cr, name="measurement_circuit")
+        for i in range(self.num_qubits):
+            qc.unitary(self._unitary_from_parameters(self._parameters[i]), [i, i + self.num_qubits])
+
+        qc.measure(qr, cr)
+
+        t2 = time.time()
+        LOGGER.info(f"Finished circuit construction. Took {t2 - t1:.6f}s")
+
+        return qc
+
+    def to_sampler_pub(
+        self,
+        circuit: QuantumCircuit,
+        circuit_binding: BindingsArray,
+        shots: int,
+        *,
+        pass_manager: StagedPassManager | None = None,
+    ) -> tuple[SamplerPub, POVMMetadata]:
+        """Append the measurement circuit(s) to the supplied circuit.
+
+        This method takes a supplied circuit and appends the measurement circuit to it.
+        An ancilla qubit is required for each qubit that we want to measure (specified by
+        :attr:`.measurement_layout`). If the input circuit has one or more idling qubit(s),
+        they will be used as measurement ancilla qubits. If more ancilla qubits are needed,
+        those will be added to the input circuit (therefore increasing its size).
+
+        .. warning::
+           The number of qubits in the input circuit may be increased due to the ancilla
+           qubits required for dilatation measurements.
+
+        Args:
+            circuit: A quantum circuit.
+            circuit_binding: A bindings array.
+            shots: A specific number of shots to run with.
+            pass_manager: An optional transpilation pass manager. After the supplied circuit has
+                been composed with the measurement circuit, the pass manager will be used to
+                transpile the composed circuit.
+
+        Returns:
+            A tuple of a sampler pub and a dictionary of metadata which includes the
+            :class:`.POVMImplementation` object itself. The metadata should contain all the
+            information necessary to extract the POVM outcomes out of raw bitstrings.
+        """
+        t1 = time.time()
+        LOGGER.info("Piecing together SamplerPub")
+
+        composed_circuit = self.compose_circuits(circuit)
+
+        if pass_manager is not None:
+            composed_circuit = pass_manager.run(composed_circuit)
+
+        pub = SamplerPub(
+            circuit=composed_circuit,
+            parameter_values=circuit_binding,
+            shots=shots,
+        )
+
+        metadata = POVMMetadata(povm_implementation=self, composed_circuit=composed_circuit)
+
+        t2 = time.time()
+        LOGGER.info(f"Finished building SamplerPub. Took {t2 - t1:.6f}s")
+
+        return (pub, metadata)
+
+    @override
+    def reshape_data_bin(self, data: DataBin) -> DataBin:
+        return data
+
+    @override
+    def _povm_outcomes(
+        self,
+        bit_array: BitArray,
+        povm_metadata: POVMMetadata,
+        *,
+        loc: int | tuple[int, ...] | None = None,
+    ) -> list[tuple[int, ...]]:
+        t1 = time.time()
+        LOGGER.info("Creating POVM outcomes")
+
+        # loc is assumed to have a length of at most pv.ndim = len(pv.shape)
+
+        povm_outcomes = [
+            self._get_outcome_label(bitstring_outcome=raw_bitstring)
+            for raw_bitstring in bit_array.get_bitstrings(loc)
+        ]
+
+        t2 = time.time()
+        LOGGER.info(f"Finished creating POVM outcomes. Took {t2 - t1:.6f}s")
+
+        return povm_outcomes
+
+    def _get_outcome_label(
+        self,
+        bitstring_outcome: str,
+    ) -> tuple[int, ...]:
+        """Transform a bitstring outcome to a POVM outcome.
+
+        Args:
+            bitstring_outcome: the raw outcome of the measurement. The order of
+                qubit is assumed to be reversed. More specifically, the first
+                :attr:`.num_qubits` bits will correspond to the ancilla qubit
+                (in reverse order) and the last :attr:`.num_qubits` bits will
+                correspond to the qubit to measure (in reverse order).
+
+        Returns:
+            A tuple of indices indicating the POVM outcomes on each qubit. For each qubit,
+            the index goes from :math:``0`` to :math:``3``.
+        """
+        return tuple(
+            2 * int(bit_q) + int(bit_a)
+            for bit_q, bit_a in zip(
+                bitstring_outcome[: self.num_qubits - 1 : -1],
+                bitstring_outcome[self.num_qubits - 1 :: -1],
+            )
+        )
+
+    def _unitary_from_parameters(self, parameters: np.ndarray) -> np.ndarray:
+        """Construct the unitary defining the dilation POVM from parameters.
+
+        Args:
+            parameters: 1D array of length 8. It should contains float values
+                that specify the parametrization of the unitary.
+
+        Returns:
+            The resulting untiary.
+        """
+        num_outcomes = 4
+        u = np.zeros((num_outcomes, num_outcomes), dtype=complex)
+
+        # construct the first column of the unitary
+        u[:, 0] = n_sphere(parameters[:3])
+        u_gs = gram_schmidt(u)
+
+        x = n_sphere(parameters[3:])
+        # construct the second column of the unitary
+        for i in range(len(x) // 2):
+            u[:, 1] += (x[2 * i] + x[2 * i + 1] * 1j) * u_gs[:, 1 + i]
+        u_gs = gram_schmidt(u)
+
+        unitary = u_gs
+
+        return unitary
diff --git a/povm_toolbox/library/povm_implementation.py b/povm_toolbox/library/povm_implementation.py
index b3a4f78..a050ae8 100644
--- a/povm_toolbox/library/povm_implementation.py
+++ b/povm_toolbox/library/povm_implementation.py
@@ -16,11 +16,13 @@ import logging
 import time
 from abc import ABC, abstractmethod
 from collections import Counter
+from copy import copy
 from typing import TYPE_CHECKING, Generic, TypeVar
 
 import numpy as np
-from qiskit.circuit import QuantumCircuit
+from qiskit.circuit import AncillaRegister, QuantumCircuit
 from qiskit.circuit.exceptions import CircuitError
+from qiskit.converters import circuit_to_dag
 from qiskit.primitives.containers import DataBin
 from qiskit.primitives.containers.bindings_array import BindingsArray
 from qiskit.primitives.containers.bit_array import BitArray
@@ -144,9 +146,21 @@ class POVMImplementation(ABC, Generic[MetadataT]):
     def compose_circuits(self, circuit: QuantumCircuit) -> QuantumCircuit:
         """Compose the circuit to sample from, with the measurement circuit.
 
+        If the measurement circuit requires some ancilla qubits, this method will inspect the input
+        circuit. If the input circuit has some idling qubits available, they will be used as ancilla
+        measurement qubits. If not enough idling qubits are available, this method will add the
+        necessary number of qubits to the input circuit before composing it with the measurement
+        circuit.
+
         Args:
             circuit: The quantum circuit to be sampled from.
 
+        Raises:
+            ValueError: if the number of qubits specified by `self.measurement_layout` does not
+                match the number of qubits on which this POVM implementation acts.
+            CircuitError: if an error has occurred when adding the classic register, used to save
+                POVM results, to the input circuit.
+
         Returns:
             The composition of the supplied quantum circuit with the :attr:`.measurement_circuit` of
             this POVM implementation.
@@ -172,11 +186,40 @@ class POVMImplementation(ABC, Generic[MetadataT]):
         # applicable) matches the number of qubits of the POVM implementation.
         if self.num_qubits != len(index_layout):
             raise ValueError(
-                f"The supplied circuit (acting on {len(index_layout)} qubits)"
-                " does not match this POVM implementation which acts on"
-                f" {self.num_qubits} qubits."
+                f"The supplied measurement layout (specifying {len(index_layout)} qubits) does not"
+                f" match this POVM implementation which acts on {self.num_qubits} qubits."
             )
 
+        # Check if the measurement circuit requires some ancilla qubits
+        if self.measurement_circuit.num_qubits > self.num_qubits:
+            index_layout = copy(index_layout)
+            # Get idle qubits in supplied circuit (to be used as ancilla for measurement circuit)
+            idle_qubits = list(circuit_to_dag(dest_circuit).idle_wires())
+            # Get the indices of the idle qubits
+            idle_index = [dest_circuit.qubits.index(qubit) for qubit in idle_qubits]
+            # Remove the idle qubits that will be measured (as specified by index_layout)
+            idle_index = [idx for idx in idle_index if idx not in index_layout]
+            idle_index.sort()
+
+            # If exactly enough idle qubits available, we use all of them
+            if self.num_qubits + len(idle_index) == self.measurement_circuit.num_qubits:
+                index_layout += idle_index
+            # If not enough idle qubits are available, we add some ancilla qubits
+            elif self.num_qubits + len(idle_index) < self.measurement_circuit.num_qubits:
+                index_layout += idle_index
+                ancilla_register = AncillaRegister(
+                    self.measurement_circuit.num_qubits - len(index_layout),
+                    name="measurement_ancilla",
+                )
+                ancilla_layout = list(
+                    range(dest_circuit.num_qubits, dest_circuit.num_qubits + ancilla_register.size)
+                )
+                index_layout += ancilla_layout
+                dest_circuit.add_register(ancilla_register)
+            # If more than enough idle qubits are available, we pick only the necessary number
+            else:
+                index_layout += idle_index[: self.measurement_circuit.num_qubits - self.num_qubits]
+
         try:
             dest_circuit.add_register(*self.measurement_circuit.cregs)
         except CircuitError as exc:
@@ -235,6 +278,7 @@ class POVMImplementation(ABC, Generic[MetadataT]):
         self,
         bit_array: BitArray,
         povm_metadata: MetadataT,
+        *,
         loc: int | tuple[int, ...] | None = None,
     ) -> list[tuple[int, ...]]:
         """Convert the raw bitstrings into POVM outcomes based on the associated metadata.
@@ -268,7 +312,7 @@ class POVMImplementation(ABC, Generic[MetadataT]):
         bit_array = self._get_bitarray(data)
 
         if loc is not None:
-            return Counter(self._povm_outcomes(bit_array, povm_metadata, loc))
+            return Counter(self._povm_outcomes(bit_array, povm_metadata, loc=loc))
 
         if bit_array.ndim == 0:
             return np.array([Counter(self._povm_outcomes(bit_array, povm_metadata))], dtype=object)
@@ -276,7 +320,7 @@ class POVMImplementation(ABC, Generic[MetadataT]):
         shape = bit_array.shape
         outcomes_array: np.ndarray = np.ndarray(shape=shape, dtype=object)
         for idx in np.ndindex(shape):
-            outcomes_array[idx] = Counter(self._povm_outcomes(bit_array, povm_metadata, idx))
+            outcomes_array[idx] = Counter(self._povm_outcomes(bit_array, povm_metadata, loc=idx))
         return outcomes_array
 
     def get_povm_outcomes_from_raw(
@@ -299,10 +343,10 @@ class POVMImplementation(ABC, Generic[MetadataT]):
         bit_array = self._get_bitarray(data)
 
         if loc is not None or bit_array.ndim == 0:
-            return self._povm_outcomes(bit_array, povm_metadata, loc)
+            return self._povm_outcomes(bit_array, povm_metadata, loc=loc)
 
         shape = bit_array.shape
         outcomes_array: np.ndarray = np.ndarray(shape=shape, dtype=object)
         for idx in np.ndindex(shape):
-            outcomes_array[idx] = self._povm_outcomes(bit_array, povm_metadata, idx)
+            outcomes_array[idx] = self._povm_outcomes(bit_array, povm_metadata, loc=idx)
         return outcomes_array
diff --git a/povm_toolbox/library/randomized_projective_measurements.py b/povm_toolbox/library/randomized_projective_measurements.py
index 067e63c..720ecb9 100644
--- a/povm_toolbox/library/randomized_projective_measurements.py
+++ b/povm_toolbox/library/randomized_projective_measurements.py
@@ -381,6 +381,7 @@ class RandomizedProjectiveMeasurements(POVMImplementation[RPMMetadata]):
         self,
         bit_array: BitArray,
         povm_metadata: RPMMetadata,
+        *,
         loc: int | tuple[int, ...] | None = None,
     ) -> list[tuple[int, ...]]:
         t1 = time.time()
diff --git a/povm_toolbox/post_processor/median_of_means.py b/povm_toolbox/post_processor/median_of_means.py
index 684f313..6a561f5 100644
--- a/povm_toolbox/post_processor/median_of_means.py
+++ b/povm_toolbox/post_processor/median_of_means.py
@@ -143,7 +143,7 @@ class MedianOfMeans(POVMPostProcessor):
 
     @override
     def _single_exp_value_and_std(
-        self, observable: SparsePauliOp, loc: int | tuple[int, ...]
+        self, observable: SparsePauliOp, *, loc: int | tuple[int, ...]
     ) -> tuple[float, float]:
         count = self.counts[loc]
         shots = sum(count.values())
diff --git a/povm_toolbox/post_processor/povm_post_processor.py b/povm_toolbox/post_processor/povm_post_processor.py
index 5e6de3a..8775bb0 100644
--- a/povm_toolbox/post_processor/povm_post_processor.py
+++ b/povm_toolbox/post_processor/povm_post_processor.py
@@ -142,6 +142,7 @@ class POVMPostProcessor:
     def get_expectation_value(
         self,
         observable: SparsePauliOp,
+        *,
         loc: int | tuple[int, ...] | None = None,
     ) -> tuple[np.ndarray, np.ndarray] | tuple[float, float]:
         """Return the expectation value and standard deviation of the given ``observable``.
@@ -160,19 +161,20 @@ class POVMPostProcessor:
             instances of :class:`numpy.ndarray`.
         """
         if loc is not None:
-            return self._single_exp_value_and_std(observable, loc)
+            return self._single_exp_value_and_std(observable, loc=loc)
         if self.counts.shape == (1,):
-            return self._single_exp_value_and_std(observable, 0)
+            return self._single_exp_value_and_std(observable, loc=0)
 
         exp_val = np.zeros(shape=self.counts.shape, dtype=float)
         std = np.zeros(shape=self.counts.shape, dtype=float)
         for idx in np.ndindex(self.counts.shape):
-            exp_val[idx], std[idx] = self._single_exp_value_and_std(observable, idx)
+            exp_val[idx], std[idx] = self._single_exp_value_and_std(observable, loc=idx)
         return exp_val, std
 
     def _single_exp_value_and_std(
         self,
         observable: SparsePauliOp,
+        *,
         loc: int | tuple[int, ...],
     ) -> tuple[float, float]:
         """Return the expectation value and standard deviation of the given ``observable``.
diff --git a/povm_toolbox/sampler/povm_pub_result.py b/povm_toolbox/sampler/povm_pub_result.py
index da1c398..5f771b2 100644
--- a/povm_toolbox/sampler/povm_pub_result.py
+++ b/povm_toolbox/sampler/povm_pub_result.py
@@ -50,7 +50,7 @@ class POVMPubResult(PubResult):
         """
         return self._metadata  # type:ignore
 
-    def get_counts(self, loc: int | tuple[int, ...] | None = None) -> np.ndarray | Counter:
+    def get_counts(self, *, loc: int | tuple[int, ...] | None = None) -> np.ndarray | Counter:
         """Get the histogram data of the result.
 
         This method will leverage :meth:`~.POVMImplementation.get_povm_counts_from_raw` from the
@@ -74,7 +74,7 @@ class POVMPubResult(PubResult):
         )
 
     def get_samples(
-        self, loc: int | tuple[int, ...] | None = None
+        self, *, loc: int | tuple[int, ...] | None = None
     ) -> np.ndarray | list[tuple[int, ...]]:
         """Get the individual POVM outcomes of the result.
 
diff --git a/povm_toolbox/utilities.py b/povm_toolbox/utilities.py
index 8a94a94..5be72f6 100644
--- a/povm_toolbox/utilities.py
+++ b/povm_toolbox/utilities.py
@@ -50,3 +50,40 @@ def double_ket_to_matrix(op_ket: np.ndarray) -> np.ndarray:
     """
     dim = int(np.sqrt(len(op_ket)))
     return op_ket.reshape((dim, dim), order="F")
+
+
+# Gram-Schmidt
+def gram_schmidt(vectors: np.ndarray) -> np.ndarray:
+    """Transform ``vectors`` into an orthonormal basis (ONB) through the Gram-Schmidt process.
+
+    Args:
+        vectors: set of vectors to transform into an ONB.
+
+    Returns:
+        The resulting orthonormal basis.
+    """
+    Q, _ = np.linalg.qr(vectors)
+    return Q
+
+
+# Unit vector on n-sphere
+def n_sphere(angles: np.ndarray) -> np.ndarray:
+    """Return a unit vector on the :math:`n`-sphere.
+
+    Args:
+        angles: set of normalized angles defining the unit vector.
+
+    Returns:
+        The resulting unit vector.
+    """
+    # dimension of the sphere
+    n = len(angles)
+    # initialize the unit vector
+    unit_vector = np.ones(n + 1)
+    for i in range(n - 1):
+        unit_vector[i] *= np.cos(np.pi * angles[i])
+        unit_vector[i + 1 :] *= np.sin(np.pi * angles[i])
+    unit_vector[-2] *= np.cos(2 * np.pi * angles[-1])
+    unit_vector[-1] *= np.sin(2 * np.pi * angles[-1])
+
+    return unit_vector


Test patch:
diff --git a/test/library/test_dilation_measurements.py b/test/library/test_dilation_measurements.py
new file mode 100644
index 0000000..f9f7403
--- /dev/null
+++ b/test/library/test_dilation_measurements.py
@@ -0,0 +1,240 @@
+# (C) Copyright IBM 2024.
+#
+# This code is licensed under the Apache License, Version 2.0. You may
+# obtain a copy of this license in the LICENSE.txt file in the root directory
+# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# Any modifications or derivative works of this code must retain this
+# copyright notice, and modified files need to carry a notice indicating
+# that they have been altered from the originals.
+
+"""Tests for the DilationMeasurements class."""
+
+from unittest import TestCase
+
+import numpy as np
+from povm_toolbox.library import (
+    DilationMeasurements,
+)
+from povm_toolbox.post_processor import POVMPostProcessor
+from povm_toolbox.sampler import POVMSampler
+from qiskit.circuit import QuantumCircuit
+from qiskit.primitives import StatevectorSampler
+from qiskit.quantum_info import SparsePauliOp
+from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager
+from qiskit_ibm_runtime import SamplerV2 as RuntimeSampler
+from qiskit_ibm_runtime.fake_provider import FakeManilaV2
+
+
+class TestDilationMeasurements(TestCase):
+    SEED = 9128346
+
+    def setUp(self) -> None:
+        super().setUp()
+
+    def test_init_errors(self):
+        """Test that the ``__init__`` method raises errors correctly."""
+        # Sanity check
+        measurement = DilationMeasurements(1, parameters=np.random.uniform(0, 1, size=8))
+        self.assertIsInstance(measurement, DilationMeasurements)
+        with self.subTest(
+            "Test invalid shape for ``parameters``, not enough parameters."
+        ) and self.assertRaises(ValueError):
+            DilationMeasurements(1, parameters=np.ones(7))
+        with self.subTest(
+            "Test invalid shape for ``parameters``, number of qubits not matching."
+        ) and self.assertRaises(ValueError):
+            DilationMeasurements(1, parameters=np.ones((2, 8)))
+        with self.subTest(
+            "Test invalid shape for ``parameters``, too many dimensions."
+        ) and self.assertRaises(ValueError):
+            DilationMeasurements(1, parameters=np.ones((1, 1, 8)))
+
+    def test_repr(self):
+        """Test that the ``__repr__`` method works correctly."""
+        mub_str = (
+            "DilationMeasurements(num_qubits=1, parameters=array"
+            "([[0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]]))"
+        )
+        povm = DilationMeasurements(1, parameters=0.1 * np.arange(8))
+        self.assertEqual(povm.__repr__(), mub_str)
+
+    def test_to_sampler_pub(self):
+        """Test that the ``to_sampler_pub`` method works correctly."""
+        num_qubits = 2
+        qc = QuantumCircuit(2)
+        qc.h(0)
+        qc.h(1)
+
+        backend = FakeManilaV2()
+        backend.set_options(seed_simulator=self.SEED)
+        povm_sampler = POVMSampler(sampler=RuntimeSampler(mode=backend))
+
+        pm = generate_preset_pass_manager(
+            optimization_level=2, backend=backend, seed_transpiler=self.SEED
+        )
+
+        measurement = DilationMeasurements(
+            num_qubits,
+            parameters=np.array(
+                [
+                    0.75,
+                    0.30408673,
+                    0.375,
+                    0.40678524,
+                    0.32509973,
+                    0.25000035,
+                    0.49999321,
+                    0.83333313,
+                ]
+            ),
+        )
+
+        job = povm_sampler.run([qc], shots=128, povm=measurement, pass_manager=pm)
+        pub_result = job.result()[0]
+
+        post_processor = POVMPostProcessor(pub_result)
+
+        observable = SparsePauliOp(["ZI"], coeffs=[1.0])
+        exp_value, std = post_processor.get_expectation_value(observable)
+        self.assertAlmostEqual(exp_value, 0.09375100309506113)
+        self.assertAlmostEqual(std, 0.15820620317705336)
+        observable = SparsePauliOp(["IZ"], coeffs=[1.0])
+        exp_value, std = post_processor.get_expectation_value(observable)
+        self.assertAlmostEqual(exp_value, 0.06250168079223002)
+        self.assertAlmostEqual(std, 0.15676579098034854)
+        observable = SparsePauliOp(["XI"], coeffs=[1.0])
+        exp_value, std = post_processor.get_expectation_value(observable)
+        self.assertAlmostEqual(exp_value, 1.0275137444747091)
+        self.assertAlmostEqual(std, 0.1612840231061351)
+
+    def test_definition(self):
+        """Test that the ``definition`` method works correctly."""
+        num_qubits = 1
+
+        # parameters defining a SIC-POVM
+        sic_parameters = np.array(
+            [[0.75, 0.30408673, 0.375, 0.40678524, 0.32509973, 0.25000035, 0.49999321, 0.83333313]]
+        )
+        # define measurement and the quantum-informational POVM
+        measurement = DilationMeasurements(num_qubits, parameters=sic_parameters)
+        povm = measurement.definition()[(0,)]
+
+        with self.subTest("Test effects"):
+            effects = np.empty((4, 2, 2), dtype=complex)
+            effects[0] = np.array(
+                [
+                    [5.00000000e-01 + 0.00000000e00j, 3.25620884e-09 - 4.42295492e-06j],
+                    [3.25620884e-09 + 4.42295492e-06j, 3.91250817e-11 - 2.67501659e-30j],
+                ]
+            )
+            effects[1] = np.array(
+                [
+                    [0.16666667 + 0.00000000e00j, 0.23570227 + 4.41719108e-06j],
+                    [0.23570227 - 4.41719108e-06j, 0.33333335 - 9.43454930e-23j],
+                ]
+            )
+            effects[2] = np.array(
+                [
+                    [0.16666666 + 0.00000000e00j, -0.11785114 - 2.04124135e-01j],
+                    [-0.11785114 + 2.04124135e-01j, 0.33333334 - 8.73465760e-18j],
+                ]
+            )
+            effects[3] = np.array(
+                [
+                    [0.16666667 + 0.00000000e00j, -0.11785113 + 2.04124140e-01j],
+                    [-0.11785113 - 2.04124140e-01j, 0.33333331 - 6.84480542e-18j],
+                ]
+            )
+            for effect, povm_operator in zip(effects, povm.operators):
+                self.assertTrue(np.allclose(povm_operator.data, effect))
+
+        with self.subTest("Test bloch vectors"):
+            bloch_vectors_check = np.array(
+                [
+                    [
+                        0.0,
+                        0.0,
+                        0.5,
+                    ],
+                    [0.47140454, 0.0, -0.16666668],
+                    [-0.23570228, 0.40824827, -0.16666668],
+                    [-0.23570226, -0.40824828, -0.16666664],
+                ]
+            )
+            self.assertTrue(np.allclose(povm.get_bloch_vectors(), bloch_vectors_check))
+
+    def test_compose_circuit(self):
+        """Test that the ``compose_circuit`` method works correctly."""
+        sampler = StatevectorSampler(seed=self.SEED)
+        povm_sampler = POVMSampler(sampler)
+        measurement = DilationMeasurements(
+            num_qubits=2,
+            parameters=np.array(
+                [
+                    0.75,
+                    0.30408673,
+                    0.375,
+                    0.40678524,
+                    0.32509973,
+                    0.25000035,
+                    0.49999321,
+                    0.83333313,
+                ]
+            ),
+        )
+        with self.subTest("No idle qubits in input circuit."):
+            qc = QuantumCircuit(2)
+            qc.h(0)
+            qc.cx(0, 1)
+            job = povm_sampler.run([qc], shots=32, povm=measurement)
+            pub_result = job.result()[0]
+            self.assertEqual(pub_result.metadata.composed_circuit.num_qubits, 4)
+            self.assertEqual(pub_result.metadata.composed_circuit.num_ancillas, 2)
+            observable = SparsePauliOp(["XI", "XX", "YY", "ZX"], coeffs=[1, 1, -1, 1])
+            post_processor = POVMPostProcessor(pub_result)
+            exp_value, std = post_processor.get_expectation_value(observable)
+            self.assertAlmostEqual(exp_value, 2.0517605907028327)
+            self.assertAlmostEqual(std, 1.1158789520748584)
+        with self.subTest("Not enough idle qubits in input circuit."):
+            qc = QuantumCircuit(3)
+            qc.h(0)
+            qc.cx(0, 1)
+            measurement.measurement_layout = [0, 1]
+            job = povm_sampler.run([qc], shots=32, povm=measurement)
+            pub_result = job.result()[0]
+            self.assertEqual(pub_result.metadata.composed_circuit.num_qubits, 4)
+            self.assertEqual(pub_result.metadata.composed_circuit.num_ancillas, 1)
+            observable = SparsePauliOp(["XI", "XX", "YY", "ZX"], coeffs=[1, 1, -1, 1])
+            post_processor = POVMPostProcessor(pub_result)
+            exp_value, std = post_processor.get_expectation_value(observable)
+            self.assertAlmostEqual(exp_value, 2.0517605907028327)
+            self.assertAlmostEqual(std, 1.1158789520748584)
+        with self.subTest("Exactly enough idle qubits in input circuit."):
+            qc = QuantumCircuit(4)
+            qc.h(0)
+            qc.cx(0, 1)
+            measurement.measurement_layout = [0, 1]
+            job = povm_sampler.run([qc], shots=32, povm=measurement)
+            pub_result = job.result()[0]
+            self.assertEqual(pub_result.metadata.composed_circuit.num_qubits, 4)
+            self.assertEqual(pub_result.metadata.composed_circuit.num_ancillas, 0)
+            observable = SparsePauliOp(["XI", "XX", "YY", "ZX"], coeffs=[1, 1, -1, 1])
+            post_processor = POVMPostProcessor(pub_result)
+            exp_value, std = post_processor.get_expectation_value(observable)
+            self.assertAlmostEqual(exp_value, 2.0517605907028327)
+            self.assertAlmostEqual(std, 1.1158789520748584)
+        with self.subTest("Too many idle qubits in input circuit."):
+            qc = QuantumCircuit(5)
+            qc.h(0)
+            qc.cx(0, 1)
+            measurement.measurement_layout = [0, 1]
+            job = povm_sampler.run([qc], shots=32, povm=measurement)
+            pub_result = job.result()[0]
+            self.assertEqual(pub_result.metadata.composed_circuit.num_qubits, 5)
+            self.assertEqual(pub_result.metadata.composed_circuit.num_ancillas, 0)
+            observable = SparsePauliOp(["XI", "XX", "YY", "ZX"], coeffs=[1, 1, -1, 1])
+            post_processor = POVMPostProcessor(pub_result)
+            exp_value, std = post_processor.get_expectation_value(observable)
+            self.assertAlmostEqual(exp_value, 2.0517605907028327)
+            self.assertAlmostEqual(std, 1.1158789520748584)
diff --git a/test/library/test_randomized_projective_measurements.py b/test/library/test_randomized_projective_measurements.py
index d00cd3b..ed27975 100644
--- a/test/library/test_randomized_projective_measurements.py
+++ b/test/library/test_randomized_projective_measurements.py
@@ -336,7 +336,7 @@ class TestRandomizedPMs(TestCase):
             measurement._get_pvm_bindings_array(pvm_idx=np.zeros(10))
 
     def test_definition(self):
-        """Test that errors in ``_get_pvm_bindings_array`` methods are raised correctly."""
+        """Test that the ``definition`` method works correctly."""
         rng = default_rng()
         num_qubits = 1
         num_pvms = 5
diff --git a/test/post_processor/test_post_processor.py b/test/post_processor/test_post_processor.py
index b8677c1..56f007d 100644
--- a/test/post_processor/test_post_processor.py
+++ b/test/post_processor/test_post_processor.py
@@ -116,7 +116,7 @@ class TestPostProcessor(TestCase):
             self.assertAlmostEqual(std, 2.3563572213988917)
         with self.subTest("Test with specified ``loc`` argument."):
             observable = SparsePauliOp(["IZ", "XX", "ZY"], coeffs=[-0.5, 1, -2])
-            exp_val, std = post_processor.get_expectation_value(observable, 0)
+            exp_val, std = post_processor.get_expectation_value(observable, loc=0)
             self.assertAlmostEqual(exp_val, -1.6406249999999998)
             self.assertAlmostEqual(std, 1.3442744428582185)
         with self.subTest("Test with default ``loc`` for parametrized circuit."):
@@ -163,6 +163,6 @@ class TestPostProcessor(TestCase):
         """Test that the ``_single_exp_value_and_std`` method works correctly."""
         observable = SparsePauliOp(["ZX", "XZ", "YY"], coeffs=[1.2, 2, -3])
         post_processor = POVMPostProcessor(self.pub_result)
-        exp_val, std = post_processor._single_exp_value_and_std(observable, 0)
+        exp_val, std = post_processor._single_exp_value_and_std(observable, loc=0)
         self.assertAlmostEqual(exp_val, 6.862499999999998)
         self.assertAlmostEqual(std, 1.9438371907630394)
diff --git a/test/test_utilities.py b/test/test_utilities.py
index ca2b9b6..0dca567 100644
--- a/test/test_utilities.py
+++ b/test/test_utilities.py
@@ -13,7 +13,12 @@
 from unittest import TestCase
 
 import numpy as np
-from povm_toolbox.utilities import double_ket_to_matrix, matrix_to_double_ket
+from povm_toolbox.utilities import (
+    double_ket_to_matrix,
+    gram_schmidt,
+    matrix_to_double_ket,
+    n_sphere,
+)
 
 
 class TestUtilities(TestCase):
@@ -28,3 +33,30 @@ class TestUtilities(TestCase):
         double_ket = np.array([0.5, 3, 6, 1, 4, 7, 2 + 1.2j, 5, 8])
         matrix = np.array([[0.5, 1, 2 + 1.2j], [3, 4, 5], [6, 7, 8]])
         self.assertTrue(np.all(matrix == double_ket_to_matrix(double_ket)))
+
+    def test_gram_schmidt(self):
+        """Test the ``gram_schmidt`` utility function."""
+        vectors = np.array([[1, 0, 0], [1, 1, 0], [1, 1, 1.5]])
+        onb = gram_schmidt(vectors)
+        self.assertTrue(
+            np.allclose(
+                onb,
+                np.array(
+                    [
+                        [-5.77350269e-01, 8.16496581e-01, -8.75605293e-17],
+                        [-5.77350269e-01, -4.08248290e-01, -7.07106781e-01],
+                        [-5.77350269e-01, -4.08248290e-01, 7.07106781e-01],
+                    ]
+                ),
+            )
+        )
+        self.assertTrue(np.allclose(np.linalg.norm(onb, axis=0), np.ones(3)))
+
+    def test_n_sphere(self):
+        """Test the ``n_sphere`` utility function."""
+        angles = np.array([0.3, 0.9, 0.1])
+        unit_vector = n_sphere(angles)
+        self.assertTrue(
+            np.allclose(unit_vector, np.array([0.58778525, -0.76942088, 0.20225425, 0.14694631]))
+        )
+        self.assertAlmostEqual(np.linalg.norm(unit_vector), 1.0)