Fixing Typos and articles (#1011)

Here some typos and articles are improved.

Co-authored-by: Paul Nation <nonhermitian@gmail.com>

Author: Aniruddha120

tutorials/noise/7_accreditation.ipynb

@@ -182,7 +182,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One can use the optional twoqubitgate arguement to switch use cx instead of cz gates and can arbitrarily change the coupling map, in order to compile to the desired device topology (which in this case might lead to more layers than expected)."
+    "One can use the optional twoqubitgate argument to switch use cx instead of cz gates and can arbitrarily change the coupling map, in order to compile to the desired device topology (which in this case might lead to more layers than expected)."
    ]
   },
   {
@@ -583,4 +583,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

tutorials/optimization/1_quadratic_program.ipynb

@@ -20,7 +20,7 @@
    "source": [
     "In this tutorial, we briefly introduce how to build optimization problems using Qiskit's optimization module.\n",
     "Qiskit introduces the `QuadraticProgram` class to make a model of an optimization problem.\n",
-    "More precicely, it deals with quadratically constrained quadratic programs given as follows:\n",
+    "More precisely, it deals with quadratically constrained quadratic programs given as follows:\n",
     "\n",
     "$$\n",
     "\\begin{align}\n",
@@ -552,4 +552,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

tutorials/optimization/2_converters_for_quadratic_programs.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Optimization problems in Qiskit's optimization module are represented with the `QuadraticProgram` class, which is generic and powerful representation for optimization problems. In general, optimization algorithms are defined for a certain formulation of a quadratic program and we need to convert our problem to the right type.\n",
+ "Optimization problems in Qiskit's optimization module are represented with the `QuadraticProgram` class, which is generic and powerful representation for optimization problems. In general, optimization algorithms are defined for a certain formulation of a quadratic program and we need to convert our problem to the right type.\n",
     "\n",
     "For instance, Qiskit provides several optimization algorithms that can handle Quadratic Unconstrained Binary Optimization (QUBO) problems. These are mapped to Ising Hamiltonians, for which Qiskit uses the `qiskit.aqua.operators` module, and then their ground state is approximated. For this optimization commonly known algorithms such as VQE or QAOA can be used as underlying routine. See the following tutorial about the [Minimum Eigen Optimizer](./3_minimum_eigen_optimizer.ipynb) for more detail. Note that also other algorithms exist that work differently, such as the `GroverOptimizer`.\n",
     "\n",
@@ -373,4 +373,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

tutorials/optimization/3_minimum_eigen_optimizer.ipynb

@@ -19,7 +19,7 @@
    "metadata": {},
    "source": [
     "An interesting class of optimization problems to be addressed by quantum computing are Quadratic Unconstrained Binary Optimization (QUBO) problems.\n",
-    "Finding the solution to a QUBO is equivalent to finding the ground state of a corresponding Ising Hamiltonian, which is an important problem not only in optimization, but also in quantum chemistry and physics. For this translation, the binary variables taking values in $\\{0, 1\\}$ are replaced by spin variables taking values in $\\{-1, +1\\}$, which allows to replace the resulting spin variables by Pauli Z matrices, and thus, an Ising Hamiltonian. For more details on this mapping we refere to [1].\n",
+"Finding the solution to a QUBO is equivalent to finding the ground state of a corresponding Ising Hamiltonian, which is an important problem not only in optimization, but also in quantum chemistry and physics. For this translation, the binary variables taking values in $\\{0, 1\\}$ are replaced by spin variables taking values in $\\{-1, +1\\}$, which allows to replace the resulting spin variables by Pauli Z matrices, and thus, an Ising Hamiltonian. For more details on this mapping we refere to [1].\n",
     "\n",
     "Qiskit provides automatic conversion from a suitable `QuadraticProgram` to an Ising Hamiltonian, which then allows to leverage all the `MinimumEigenSolver` such as\n",
     "- `VQE`,\n",

tutorials/optimization/4_grover_optimizer.ipynb

@@ -55,7 +55,7 @@
     "\n",
     "where $|x\\rangle$ is the binary encoding of the integer $x$. \n",
     "\n",
-    "At each iteration in which the threshold $y$ is updated, we adapt $A_y$ such that the function values are shifted up or down (for minimum and maxmimum respectively) by $y$. For example, in the context of finding the minimum, as the value of $y$ decreases, the search space (negative values) also decreases, until only the minimum value remains. A concrete example will be explored in the next section."
+    "At each iteration in which the threshold $y$ is updated, we adapt $A_y$ such that the function values are shifted up or down (for minimum and maximum respectively) by $y$. For example, in the context of finding the minimum, as the value of $y$ decreases, the search space (negative values) also decreases, until only the minimum value remains. A concrete example will be explored in the next section."
    ]
   },
   {
@@ -279,4 +279,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

tutorials/optimization/5_admm_optimizer.ipynb

@@ -347,7 +347,7 @@
    "source": [
     "## Quantum Solution\n",
     "We now solve the same optimization problem with QAOA as QUBO optimizer, running on simulated quantum device. \n",
-    "First, one need to select the classical optimizer of the eigensolver QAOA. Then, the simulation backened is set. Finally, \n",
+    "First, one need to select the classical optimizer of the eigensolver QAOA. Then, the simulation backend is set. Finally, \n",
     "the eigensolver is wrapped into the `MinimumEigenOptimizer` class. A new instance of `ADMMOptimizer` is populated with QAOA as QUBO optimizer."
    ]
   },
@@ -530,4 +530,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

tutorials/optimization/6_examples_max_cut_and_tsp.ipynb

@@ -235,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Qiskit provides funcitonality to direclty generate the Ising Hamiltonian as well as create the corresponding `QuadraticProgram`. "
+    "Qiskit provides functionality to directly generate the Ising Hamiltonian as well as create the corresponding `QuadraticProgram`. "
    ]
   },
   {

tutorials/pulse/3_building_pulse_schedules.ipynb

@@ -132,7 +132,7 @@
    "source": [
     "### `append` or `+`\n",
     "\n",
-    "The `append` method is like `insert`, but the insertion time is determined for us. The `Instruction` or `Schedule` being added will begin when all the channels common to the two become free. If they contain no common channels, then the `Schedule` will be appended at `time=0`. In psuedocode:\n",
+    "The `append` method is like `insert`, but the insertion time is determined for us. The `Instruction` or `Schedule` being added will begin when all the channels common to the two become free. If they contain no common channels, then the `Schedule` will be appended at `time=0`. In pseudocode:\n",
     "\n",
     "```\n",
     "time = 0\n",
@@ -330,4 +330,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

tutorials/pulse/4_adding_measurements.ipynb

@@ -6,14 +6,14 @@
    "source": [
     "# Adding measurements to `Schedule`s\n",
     "\n",
-    "Measurement is clearly a very important part of building a Pulse schedule -- this is required to get the results of our program execution! The powerful low-level control we are granted by Pulse gives us more freedom than `QuantumCircuit`s in specifying how the measurement should be done, enabling you to explore readout error mitigation. This power of course comes with responsibility: we have to understand how measurement works, and accomodate certain hardware constraints.\n",
+    "Measurement is clearly a very important part of building a Pulse schedule -- this is required to get the results of our program execution! The powerful low-level control we are granted by Pulse gives us more freedom than `QuantumCircuit`s in specifying how the measurement should be done, enabling you to explore readout error mitigation. This power of course comes with responsibility: we have to understand how measurement works, and accommodate certain hardware constraints.\n",
     "\n",
     "On this page, we will explore in depth how to create measurements, using several different approaches of increasing complexity.\n",
     "\n",
-    "**Note: Pulse allows you to receive raw, kerneled, and disciminated readout data (whereas circuits will only return discriminated data).\n",
+    "**Note: Pulse allows you to receive raw, kerneled, and discriminated readout data (whereas circuits will only return discriminated data).\n",
     "\n",
     "### Adding a backend-default measurement with `measure`\n",
-    "To add measurements as easily to `Schedule`s as to `QuantumCircuit`s, you just have to know which qubits you want to measure (below, qubits 0 and 1) and have a OpenPulse-enabled `backend`:\n",
+    "To add measurements as easily to `Schedule`s as to `QuantumCircuit`s, you just have to know which qubits you want to measure (below, qubits 0 and 1) and have an OpenPulse-enabled `backend`:\n",
     "\n",
     "```\n",
     "# Appending a measurement schedule to a Schedule, sched\n",
@@ -71,7 +71,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each qubit has two channels related to readout, as we see above. These are the readout transmit `MeasureChannel`s, and the readout receive `AcquireChannel`s. In superconducting qubit architectures, qubits are coupled to readout resonators. The `MeasureChannel` and `AcquireChannel`s label signal lines which connect to the readout resonator. The coupling between the qubit and the readout resonator hybridizes their state, so when a stimulus pulse is sent to the readout resonantor, the reflected pulse is dependent on the state of the qubit. The acquisition \"pulse\" is truly a trigger specifying to the analog-to-digital converter (ADC) to begin collecting data, and for how long. That data is used to classify the qubit state.\n",
+    "Each qubit has two channels related to readout, as we see above. These are the readout transmit `MeasureChannel`s, and the readout receive `AcquireChannel`s. In superconducting qubit architectures, qubits are coupled to readout resonators. The `MeasureChannel` and `AcquireChannel`s label signal lines which connect to the readout resonator. The coupling between the qubit and the readout resonator hybridizes their state, so when a stimulus pulse is sent to the readout resonator, the reflected pulse is dependent on the state of the qubit. The acquisition \"pulse\" is truly a trigger specifying to the analog-to-digital converter (ADC) to begin collecting data, and for how long. That data is used to classify the qubit state.\n",
     "\n",
     "### Specifying classical memory slots\n",
     "\n",
@@ -292,4 +292,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}