Qiskit · abbycross · Aug 18, 2025 · Aug 15, 2025 · Aug 15, 2025 · Aug 18, 2025
diff --git a/docs/guides/instances.ipynb b/docs/guides/instances.ipynb
@@ -101,7 +101,7 @@
     "1. Select the plan this instance is associated with (for example, Premium Plan), then enter the number of minutes to allocate to this instance. The unallocated time available to use appears under the allocation entry box. If this instance is not associated with a free plan, a cost will be incurred when this instance is used to run workloads.\n",
     "1. Click **Next**. A list of the QPUs the instance can access is shown. If you want the instance to have access to all the QPUs listed (and all QPUs added to this plan in the future), click **Next**. To customize which QPUs can be accessed with this instance, click the \"Customize allocated compute resources\" toggle. Select specific QPUs that this instance can access, then click **Next**.\n",
     "   <Admonition type=\"note\">\n",
-    "      If you customize the allocated compute resources, the available QPUs will never be automatically updated, regardless of any changes made to the parent plan.  However, you can manually add or remove QPUs later. If you do not customize them, you will always have access to any QPU the account has access on the plan. So if in the future there is a new system added to the plan, the instance automatically has access to it.\n",
+    "      If you customize the allocated compute resources, the available QPUs will never be automatically updated, regardless of any changes made to the parent plan.  However, you can manually add or remove QPUs later. If you do not customize them, you will always have access to any QPU the account has access on the plan. So if in the future there is a new QPU added to the plan, the instance automatically has access to it.\n",
     "    </Admonition>\n",
     "1. Click **Create instance**. You can now view the instance on the [Instances](https://quantum.cloud.ibm.com/instances) page. If you have set up multiple plans on your account, each plan type has its own tab on the Instances table.\n",
     "\n",

diff --git a/docs/migration-guides/classic-iqp-to-cloud-iqp.mdx b/docs/migration-guides/classic-iqp-to-cloud-iqp.mdx
@@ -88,7 +88,7 @@ Follow these steps to set up the new version of IBM Quantum Platform.
    1. Specify a name and choose the type of plan you want. Scroll down to see the **Terms** link.  After reading the terms, select the **agree** checkbox and click **Next**.
    1. On the Plan inheritance page, leave the default setting (toggle is not enabled) and click **Next** if you're setting up an Open instance.  If you are not setting up an Open instance and you want a fixed set of QPUs to be available to this plan, enable the "Customize allocated compute resources" toggle, choose the QPUs you want this instance to be able to access, then click **Next**.
       <Admonition type="note">
-      If you customize the allocated compute resources, the available QPUs will never be automatically updated, regardless of any changes made to the parent plan.  However, you can manually add or remove QPUs later. If you do not customize them, you will always have access to any QPU the account has access on the plan. So if in the future there is a new system added to the plan, the instance automatically has access to it.
+      If you customize the allocated compute resources, the available QPUs will never be automatically updated, regardless of any changes made to the parent plan.  However, you can manually add or remove QPUs later. If you do not customize them, you will always have access to any QPU the account has access on the plan. So if in the future there is a new QPU added to the plan, the instance automatically has access to it.
       </Admonition>
    1. Choose whether you want an access group to be created for this instance and click **Create instance**.  For an Open instance, you probably don't need an access group, although creating one won't impact your usage unless you add another user to the group. You can add or remove access groups at any time from IBM Cloud. To learn more, see the [Setting up access groups](https://cloud.ibm.com/docs/account?topic=account-groups)  topic.
 

diff --git a/docs/migration-guides/qiskit-quantum-instance.mdx b/docs/migration-guides/qiskit-quantum-instance.mdx
@@ -41,7 +41,7 @@ The remainder of this guide focused on the [`qiskit.utils.QuantumInstance.execut
 <Admonition type="caution">
   **Background on the Qiskit primitives**
 
-  The Qiskit primitives are algorithmic abstractions that encapsulate system or simulator access for easy integration into algorithm workflows.
+  The Qiskit primitives are algorithmic abstractions that encapsulate quantum processing unit (QPU) or simulator access for easy integration into algorithm workflows.
 
   There are two types of primitives: Sampler and Estimator.
 
@@ -348,7 +348,7 @@ print(expectation_value)
 [-0.04101562]
 ```
 
-### Example 3: Circuit sampling on IBM system with error mitigation
+### Example 3: Circuit sampling on IBM QPU with error mitigation
 
 **QuantumInstance**
 

diff --git a/docs/tutorials/combine-error-mitigation-techniques.ipynb b/docs/tutorials/combine-error-mitigation-techniques.ipynb
@@ -573,7 +573,7 @@
   }
  ],
  "metadata": {
-  "description": "Combine error mitigation options for utility-scale experiments using 100Q+ IBM Quantum systems and the Qiskit Runtime Estimator primitive.",
+  "description": "Combine error mitigation options for utility-scale experiments using 100Q+ IBM Quantum QPUs and the Qiskit Runtime Estimator primitive.",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

diff --git a/docs/tutorials/grovers-algorithm.ipynb b/docs/tutorials/grovers-algorithm.ipynb
@@ -371,7 +371,7 @@
   }
  ],
  "metadata": {
-  "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.",
+  "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and QPUs to solve real-world problems.",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

diff --git a/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb b/docs/tutorials/quantum-approximate-optimization-algorithm.ipynb
@@ -1398,7 +1398,7 @@
   }
  ],
  "metadata": {
-  "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and systems to solve real-world problems.",
+  "description": "Learn the basics of quantum computing, and how to use IBM Quantum services and QPUs to solve real-world problems.",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",

diff --git a/...ning/courses/quantum-business-foundations/introduction-to-quantum-computing.mdx b/...ning/courses/quantum-business-foundations/introduction-to-quantum-computing.mdx
@@ -88,13 +88,13 @@ While some may consider quantum computing an innovative area at the beginning of
 | <em>1981</em> | Richard Feynman, a noted theoretical physicist, identified the potential for quantum computers as far back as 1981. At the first Conference on the Physics of Computation, organized by IBM and the Massachusetts Institute of Technology (MIT), he famously closed his keynote speech with the statement “[...] nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.” [\[1\]](#sources)|
 | <em> 1994 </em> | In 1994, Peter Shor, a mathematician then at AT&amp;T Bell Labs in New Jersey, proved that a fully functional quantum computer could do something remarkable: it could crack RSA encryption, a popular means of securing private communications. He showed that his quantum algorithm could do in minutes what might take a regular computer the lifetime of the universe to unravel. <sup>2</sup>|
 | <em> 1996 </em> | A year later, Lov Grover, also a Bell Labs scientist, came up with a quantum algorithm that would allow people to swiftly search unstructured databases. Scientists piled into the field, and advances in hardware soon followed the breakthroughs in code. [\[2\]](#sources)|
-| <em> 1998 </em> | The first experimental demonstration of a quantum algorithm was achieved in 1998. A working 2–qubit nuclear magnetic resonance (NMR) quantum computer was used to solve Deutsch’s problem by Jonathan A. Jones and Michele Mosca at Oxford University and shortly after by Isaac L. Chuang at IBM’s Almaden Research Center and Mark Kubinec and the University of California, Berkeley, together with coworkers at Stanford University and MIT. [\[3\]](#sources)|
-| <em> 2001 </em> | 2001 saw the first execution of Shor’s algorithm at IBM’s Almaden Research Center and Stanford University. The number 15 was factored using 1018 identical molecules, each containing seven active nuclear spins. [\[4\]](#sources)|
+| <em> 1998 </em> | The first experimental demonstration of a quantum algorithm was achieved in 1998. A working 2–qubit nuclear magnetic resonance (NMR) quantum computer was used to solve Deutsch’s problem by Jonathan A. Jones and Michele Mosca at Oxford University and shortly after by Isaac L. Chuang at the IBM Almaden Research Center and Mark Kubinec and the University of California, Berkeley, together with coworkers at Stanford University and MIT. [\[3\]](#sources)|
+| <em> 2001 </em> | 2001 saw the first execution of Shor’s algorithm at the IBM Almaden Research Center and Stanford University. The number 15 was factored using 1018 identical molecules, each containing seven active nuclear spins. [\[4\]](#sources)|
 | <em> 2005 </em> | By the mid–2000s, the research field had developed several types of superconducting qubits, each with its own advantages and drawbacks. In 2007, a team at Yale found a way to combine some of these approaches to overcome their individual shortcomings, naming the new design the “transmon qubit.” The transmon qubit would go on to be at the heart of many companies’ efforts to develop quantum computers, including IBM Quantum, Google AI, and Rigetti Computing. A member of the Yale team, Jay Gambetta, later became Vice President of Quantum Computing for IBM Research. |
 
-![IBM’s four-qubit quantum computer](/learning/images/courses/quantum-business-foundations/introduction-to-quantum-computing/fourqubitdevice_small.avif)
+![The IBM four-qubit quantum computer](/learning/images/courses/quantum-business-foundations/introduction-to-quantum-computing/fourqubitdevice_small.avif)
 
-*Layout of IBM’s four-qubit superconducting quantum computer announced in 2015. (Credit: IBM Research)*
+*Layout of the IBM four-qubit superconducting quantum computer announced in 2015. (Credit: IBM Research)*
 
 
 |         |       |

diff --git a/learning/courses/quantum-business-foundations/quantum-computing-fundamentals.mdx b/learning/courses/quantum-business-foundations/quantum-computing-fundamentals.mdx
@@ -154,7 +154,7 @@ True or false: Quantum circuits are not physical devices.
 
 </summary>
 
-True. A quantum circuit is an abstract representation of a set of instructions that make up a quantum algorithm. We can use a visual tool like IBM’s Quantum Composer or a programming language like Qiskit to construct quantum circuits..
+True. A quantum circuit is an abstract representation of a set of instructions that make up a quantum algorithm. We can use a visual tool like the IBM [Composer](https://quantum.cloud.ibm.com/composer) or a programming language like [Qiskit](/docs/guides) to construct quantum circuits.
 
 </details>
 

diff --git a/learning/courses/quantum-chem-with-vqe/ground-state.ipynb b/learning/courses/quantum-chem-with-vqe/ground-state.ipynb
@@ -489,7 +489,7 @@
     "\n",
     "Finally, we have **x**. This is the vector of variational parameters. These are the parameters used in the calculation that yielded the minimum cost function (energy expectation value). These eight values correspond to the eight rotation angles in those gates in the ansatz that take variable rotation angles.\n",
     "\n",
-    "Congratulations! You have run a VQE calculation on an IBM Quantum System!\n",
+    "Congratulations! You have run a VQE calculation on an IBM Quantum QPU!\n",
     "\n",
     "In the next lesson, we will see how to adjust this workstream to include variables in your Hamiltonian. In the context of quantum chemistry problems, this might mean varying geometry to determine shapes of molecules or binding sites."
    ]

diff --git a/learning/courses/quantum-chem-with-vqe/hamiltonian-construction.ipynb b/learning/courses/quantum-chem-with-vqe/hamiltonian-construction.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "As someone working in quantum chemistry, you probably already have your favorite software for modeling molecules, which can generate a Hamiltonian that describes your system of interest. Here, we will use code built solely on PySCF, numpy, and Qiskit. But the process of Hamiltonian preparation transfers to prepackaged solutions as well. The only difference between this approach and other software will be minor syntax differences; some of these are addressed in the \"Third-party software\" subsection to facilitate integration of existing workflows.\n",
     "\n",
-    "Generating a quantum chemistry Hamiltonian for use on IBM Quantum&reg; systems involves the following steps:\n",
+    "Generating a quantum chemistry Hamiltonian for use on IBM Quantum&reg; QPUs involves the following steps:\n",
     "\n",
     "1. Define your molecule (geometry, spin, active space, and so on)\n",
     "2. Generate the fermionic Hamiltonian (creation and annihilation operators)\n",
@@ -291,7 +291,7 @@
    "id": "928d72cf-212f-4f85-9d81-a7ffb33d6db8",
    "metadata": {},
    "source": [
-    "These Hamiltonians are currently fermionic (creation and annihilation) operators, applicable to systems of (indistinguishable) fermions, and correspondingly subject to antisymmetry under exchange. This results in different statics than would apply to a distinguishable or bosonic system. To run calculations on IBM Quantum Systems, we require a bosonic operator describing the energy. The result of such a mapping is conventionally written in terms of Pauli operators, since they are both Hermitian and unitary. There are several mappings one can use. One of the simplest is the Jordan Wigner transformation.\n",
+    "These Hamiltonians are currently fermionic (creation and annihilation) operators, applicable to systems of (indistinguishable) fermions, and correspondingly subject to antisymmetry under exchange. This results in different statics than would apply to a distinguishable or bosonic system. To run calculations on IBM Quantum QPUs, we require a bosonic operator describing the energy. The result of such a mapping is conventionally written in terms of Pauli operators, since they are both Hermitian and unitary. There are several mappings one can use. One of the simplest is the Jordan Wigner transformation.\n",
     "\n",
     "3. Mapping the Hamiltonian\n",
     "\n",

diff --git a/learning/courses/quantum-diagonalization-algorithms/introduction.ipynb b/learning/courses/quantum-diagonalization-algorithms/introduction.ipynb
@@ -6,7 +6,7 @@
    "metadata": {
     "gloss": {
      "computational-basis-state": {
-      "text": "Also known as Z-basis states, these are the states we measure when we measure in the Z (or 'computational') basis. These are the states with labels like |00〉 and |00110100〉. IBM&reg; systems always measure in the Z-basis.",
+      "text": "Also known as Z-basis states, these are the states we measure when we measure in the Z (or 'computational') basis. These are the states with labels like |00〉 and |00110100〉. IBM&reg; quantum computers always measure in the Z-basis.",
       "title": "computational basis state"
      },
      "features": {

diff --git a/learning/courses/quantum-machine-learning/data-encoding.ipynb b/learning/courses/quantum-machine-learning/data-encoding.ipynb
@@ -1550,7 +1550,7 @@
     "\n",
     "A hardware-efficient feature mapping is one that takes into account constraints of real quantum computers, in the interest of reducing noise and errors in the computation. When running quantum circuits on near-term quantum computers, there are many strategies to mitigate noise inherent to the hardware. One main strategy for hardware efficiency is the minimization of the depth of the quantum circuit so that noise and decoherence have less time to corrupt the computation. The depth of a quantum circuit is the number of time-aligned gate steps required to complete the entire computation (after circuit optimization)[\\[5\\]](#references). Recall that the depth of the abstract, logical circuit may be much lower than the depth once the circuit is transpiled for a real quantum computer.\n",
     "\n",
-    "Transpilation is the process of converting the quantum circuit from a high-level abstraction to one that is ready to run on a real quantum computer, taking into account constraints of the hardware. A quantum computer has a native set of single- and two-qubit gates. This means all gates in Qiskit code have to be transpiled into the set of native hardware gates. For example, in ibm_torino, a system sporting a heron r1 processor and completed in 2023, the native or basis gates are `{CZ, ID, RZ, SX, X}`. These are the two-qubit controlled-Z gate, and single-qubit gates called identity, $Z$-rotation, square root of NOT, and NOT, respectively, providing a universal set. When implementing multi-qubit gates as an equivalent subcircuit, physical two-qubit $CZ$ gates are required, along with other single-qubit gates available in hardware. In addition, to perform a two-qubit gate on a pair of qubits that are not physically coupled, SWAP gates are added to move qubit states between qubits to enable coupling, which leads to an unavoidable extension of the circuit. Using the ```optimization``` argument that can be set from 0 up to a highest level of 3. For greater control and customizability, the transpiler pipeline can be managed with the [Qiskit Pass Manager](/docs/api/qiskit/qiskit.transpiler.PassManager). Refer to the [Qiskit Transpiler documentation](/docs/api/qiskit/transpiler) for more information on transpilation.\n",
+    "Transpilation is the process of converting the quantum circuit from a high-level abstraction to one that is ready to run on a real quantum computer, taking into account constraints of the hardware. A quantum computer has a native set of single- and two-qubit gates. This means all gates in Qiskit code have to be transpiled into the set of native hardware gates. For example, in ibm_torino, a QPU sporting a Heron r1 processor and completed in 2023, the native or basis gates are `{CZ, ID, RZ, SX, X}`. These are the two-qubit controlled-Z gate, and single-qubit gates called identity, $Z$-rotation, square root of NOT, and NOT, respectively, providing a universal set. When implementing multi-qubit gates as an equivalent subcircuit, physical two-qubit $CZ$ gates are required, along with other single-qubit gates available in hardware. In addition, to perform a two-qubit gate on a pair of qubits that are not physically coupled, SWAP gates are added to move qubit states between qubits to enable coupling, which leads to an unavoidable extension of the circuit. Using the ```optimization``` argument that can be set from 0 up to a highest level of 3. For greater control and customizability, the transpiler pipeline can be managed with the [Qiskit Pass Manager](/docs/api/qiskit/qiskit.transpiler.PassManager). Refer to the [Qiskit Transpiler documentation](/docs/api/qiskit/transpiler) for more information on transpilation.\n",
     "\n",
     "In Havlicek et al. 2019 [\\[2\\]](#references), one way the authors achieve hardware efficiency is by using the $ZZ$ feature map because it is a second-order expansion (see the “$ZZ$ feature map” section above). An $N$-order expansion has $N$-qubit gates. IBM&reg; quantum computers do not have native $N$-qubit gates, where $N>2$, so to implement them would require decomposition into two-qubit CNOT gates available in hardware. A second way the authors minimize depth is by choosing a $ZZ$ coupling topology that maps directly to the architecture couplings. A further optimization they undertake is targeting a higher-performing, suitably connected hardware subcircuit. Additional things to consider are minimizing the number of feature map repetitions and choosing a customized low-depth or “linear” entangling scheme instead of the “full” scheme that entangles all qubits.\n",
     "\n",

diff --git a/learning/courses/quantum-machine-learning/introduction.ipynb b/learning/courses/quantum-machine-learning/introduction.ipynb
@@ -6,7 +6,7 @@
    "metadata": {
     "gloss": {
      "computational-basis-state": {
-      "text": "Also known as Z-basis states, these are the states we measure when we measure in the Z (or 'computational') basis. These are the states with labels like |00〉 and |00110100〉. IBM&reg; systems always measure in the Z-basis.",
+      "text": "Also known as Z-basis states, these are the states we measure when we measure in the Z (or 'computational') basis. These are the states with labels like |00〉 and |00110100〉. IBM&reg; quantum computers always measure in the Z-basis.",
       "title": "computational basis state"
      },
      "features": {