Add license agreement links to Qiskit Functions overview

docs/guides/functions.mdx

@@ -31,18 +31,18 @@ Functions are provided by IBM® and third-party partners. Each is performant
 
 | Name | Provider | Recommended use | Unique benefits |
 |---|---|---|---|
-| Tensor-Network Error Mitigation<br/><br/>[Guide](/docs/guides/algorithmiq-tem)<br/>[API reference](/docs/api/functions/algorithmiq-tem)      | Algorithmiq  | Workloads that have low-weight observables and loop-free circuits. | Reduces measurement overhead and variance, outperforming standard error mitigation baselines such as Zero Noise Extrapolation (ZNE) and Probabilistic Error Cancellation (PEC) for relevant circuit classes. |
+| Tensor-Network Error Mitigation<br/><br/>[Guide](/docs/guides/algorithmiq-tem)<br/>[API reference](/docs/api/functions/algorithmiq-tem)<br/>[License](https://ibm.box.com/s/cbvfdhv3i0vnwv4sxkdpp3pcvip27atd)      | Algorithmiq  | Workloads that have low-weight observables and loop-free circuits. | Reduces measurement overhead and variance, outperforming standard error mitigation baselines such as Zero Noise Extrapolation (ZNE) and Probabilistic Error Cancellation (PEC) for relevant circuit classes. |
 | QESEM: Error Suppression and Error Mitigation<br/><br/>[Guide](/docs/guides/qedma-qesem)<br/>[API reference](/docs/api/functions/qedma-qesem)    | Qedma        | Workloads that  include circuits with fractional or parameterized gates, high-weight observables,  and workflows that require unbiased expectation values and accurate runtime estimates. | Produces unbiased expectation values with lower variance and resource overhead, outperforming ZNE and PEC for relevant circuit classes. |
-| Performance Management<br/><br/>[Guide](/docs/guides/q-ctrl-performance-management)<br/>[API reference](/docs/api/functions/q-ctrl-performance-management) | Q-CTRL       | Workloads that contain parametric circuits, deep circuits, or require many circuit executions. | Automatically applies AI-driven error suppression to quantum algorithms, maximizing the performance of IBM devices to deliver accurate results while reducing the number of shots, compute time, and cost required. <br/><br/>Zero-overhead method that improves execution accuracy for the Sampler and the Estimator primitives, compatible with any weight of observables. |
+| Performance Management<br/><br/>[Guide](/docs/guides/q-ctrl-performance-management)<br/>[API reference](/docs/api/functions/q-ctrl-performance-management)<br/>[License](https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z) | Q-CTRL       | Workloads that contain parametric circuits, deep circuits, or require many circuit executions. | Automatically applies AI-driven error suppression to quantum algorithms, maximizing the performance of IBM devices to deliver accurate results while reducing the number of shots, compute time, and cost required. <br/><br/>Zero-overhead method that improves execution accuracy for the Sampler and the Estimator primitives, compatible with any weight of observables. |
 
 <span id="application"></span>
 ### Application functions
 
 | Name | Provider | Recommended use | Unique benefits |
 |---|---|---|---|
-| QUICK-PDE<br/><br/>[Guide](/docs/guides/colibritd-pde)<br/>[API reference](/docs/api/functions/colibritd-pde) | ColibriTD            | Use quantum computation for multi-physics PDEs.<br/><br/>Prepare simulation workflows for quantum hardware, while keeping full control over both quantum and physical modeling parameters.                     | Offers a robust hybrid VQA framework that delivers precise, scalable PDE solutions through advanced solution encoding and spectral methods, making it an ideal entry point for teams trying to build quantum-ready simulation capabilities. |
-| Quantum Portfolio Optimizer <br/><br/>[Guide](/docs/guides/global-data-quantum-optimizer)<br/>[API reference](/docs/api/functions/global-data-quantum-optimizer)  | Global Data Quantum  | Workloads for financial optimization, seeking optimal portfolio strategies over time while minimizing risk and maximizing returns, enabling trading strategy back-testing.                                   | Solves combinatorial optimization problems through a highly specialized adaptation of the VQE quantum algorithm for this financial use case, using optimized execution strategies and optimizers, along with noise-aware error mitigation techniques tailored to portfolio optimization. |
-| HI-VQE Chemistry<br/><br/>[Guide](/docs/guides/qunova-chemistry)<br/>[API reference](/docs/api/functions/qunova-chemistry)   | Qunova Computing     | Workloads in computational chemistry, molecular simulation, materials science, or any Hamiltonian simulation that require solving many-body electronic structure problems.                                   | Solves molecular electronic structures by using enhanced SQD with achieving chemical accuracy (1 kcal/mol, 1.6 mHa) for problems modeled with 40 to 60 qubits, outperforming some classical solutions on supercomputers or standard SQD in convergence speed or accuracy, respectively, by orders of magnitude.                                                                                                                                               |
-| Iskay Quantum Optimizer<br/><br/>[Guide](/docs/guides/kipu-optimization)<br/>[API reference](/docs/api/functions/kipu-optimization) | Kipu Quantum         | Optimization workloads such as  scheduling, logistics, routing, and QUBO/HUBO problems. <br/><br/>  | Integrated tunable classical pre- and post-processing methods for the quantum optimization routine. <br/><br/>Delivers runtime advantage over classical solvers (CPLEX, simulated annealing, and tabu search) on selected HUBO benchmarks. <br/><br/>Market Split `ms_5_100`, a hard challenge, solved within hours (see [this tutorial](/docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer)).                                        |
-| Singularity Machine Learning<br/><br/>[Guide](/docs/guides/multiverse-computing-singularity)<br/>[API reference](/docs/api/functions/multiverse-computing-singularity)  | Multiverse Computing | Classical machine learning classification workflows that could benefit from improved accuracy or computational efficiency by leveraging quantum optimization executed on IBM hardware.               | Delivers accuracy comparable to or exceeding classical models such as Random Forest or XGBoost, while operating with significantly fewer learners and a more compact ensemble. <br/><br/>Powered by quantum-optimized voting, it selects the most informative learners and refines decision boundaries, resulting in greater efficiency, reduced model complexity, and more robust performance. |
-| Optimization Solver<br/><br/>[Guide](/docs/guides/q-ctrl-optimization-solver)<br/>[API reference](/docs/api/functions/q-ctrl-optimization-solver)             | Q-CTRL               | Binary optimization problems or any combinatorial problem that can be mapped to a binary cost function.  <br/><br/>Cost functions of any order and problem sizes up to the maximum device scale are supported. | Noise-aware, end-to-end quantum optimization solution that enables inputs of high-level problem definitions and automatically finds accurate solutions to classically challenging combinatorial problems on utility-scale quantum hardware. <br/><br/>It abstracts away complexity by handling error suppression, efficient mapping, and hybrid quantum-classical optimization to solve optimization tasks at full device scale without deep quantum expertise. |
+| QUICK-PDE<br/><br/>[Guide](/docs/guides/colibritd-pde)<br/>[API reference](/docs/api/functions/colibritd-pde)<br/>[License](https://ibm.box.com/s/eylihre0nf2lskzteyabd7y7v7cu5bb3) | ColibriTD            | Use quantum computation for multi-physics PDEs.<br/><br/>Prepare simulation workflows for quantum hardware, while keeping full control over both quantum and physical modeling parameters.                     | Offers a robust hybrid VQA framework that delivers precise, scalable PDE solutions through advanced solution encoding and spectral methods, making it an ideal entry point for teams trying to build quantum-ready simulation capabilities. |
+| Quantum Portfolio Optimizer <br/><br/>[Guide](/docs/guides/global-data-quantum-optimizer)<br/>[API reference](/docs/api/functions/global-data-quantum-optimizer)<br/>[License](https://ibm.box.com/s/t9tj2t8ey8hh1iep3jr88yzlqbqdvq78)  | Global Data Quantum  | Workloads for financial optimization, seeking optimal portfolio strategies over time while minimizing risk and maximizing returns, enabling trading strategy back-testing.                                   | Solves combinatorial optimization problems through a highly specialized adaptation of the VQE quantum algorithm for this financial use case, using optimized execution strategies and optimizers, along with noise-aware error mitigation techniques tailored to portfolio optimization. |
+| HI-VQE Chemistry<br/><br/>[Guide](/docs/guides/qunova-chemistry)<br/>[API reference](/docs/api/functions/qunova-chemistry)<br/>[License](https://ibm.box.com/s/7d36ty2r5ecmnnu1bvk4kxx5grbz2mwz)   | Qunova Computing     | Workloads in computational chemistry, molecular simulation, materials science, or any Hamiltonian simulation that require solving many-body electronic structure problems.                                   | Solves molecular electronic structures by using enhanced SQD with achieving chemical accuracy (1 kcal/mol, 1.6 mHa) for problems modeled with 40 to 60 qubits, outperforming some classical solutions on supercomputers or standard SQD in convergence speed or accuracy, respectively, by orders of magnitude.                                                                                                                                               |
+| Iskay Quantum Optimizer<br/><br/>[Guide](/docs/guides/kipu-optimization)<br/>[API reference](/docs/api/functions/kipu-optimization)<br/>[License](https://ibm.box.com/s/h7ocmsqgwjuj493b46m1nrkqkazh4g4j) | Kipu Quantum         | Optimization workloads such as  scheduling, logistics, routing, and QUBO/HUBO problems. <br/><br/>  | Integrated tunable classical pre- and post-processing methods for the quantum optimization routine. <br/><br/>Delivers runtime advantage over classical solvers (CPLEX, simulated annealing, and tabu search) on selected HUBO benchmarks. <br/><br/>Market Split `ms_5_100`, a hard challenge, solved within hours (see [this tutorial](/docs/tutorials/solve-market-split-problem-with-iskay-quantum-optimizer)).                                        |
+| Singularity Machine Learning<br/><br/>[Guide](/docs/guides/multiverse-computing-singularity)<br/>[API reference](/docs/api/functions/multiverse-computing-singularity)<br/>[License](https://ibm.box.com/s/oe621o30gxvpo8v0tivp31075w9wc6t8)  | Multiverse Computing | Classical machine learning classification workflows that could benefit from improved accuracy or computational efficiency by leveraging quantum optimization executed on IBM hardware.               | Delivers accuracy comparable to or exceeding classical models such as Random Forest or XGBoost, while operating with significantly fewer learners and a more compact ensemble. <br/><br/>Powered by quantum-optimized voting, it selects the most informative learners and refines decision boundaries, resulting in greater efficiency, reduced model complexity, and more robust performance. |
+| Optimization Solver<br/><br/>[Guide](/docs/guides/q-ctrl-optimization-solver)<br/>[API reference](/docs/api/functions/q-ctrl-optimization-solver)<br/>[License](https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z)             | Q-CTRL               | Binary optimization problems or any combinatorial problem that can be mapped to a binary cost function.  <br/><br/>Cost functions of any order and problem sizes up to the maximum device scale are supported. | Noise-aware, end-to-end quantum optimization solution that enables inputs of high-level problem definitions and automatically finds accurate solutions to classically challenging combinatorial problems on utility-scale quantum hardware. <br/><br/>It abstracts away complexity by handling error suppression, efficient mapping, and hybrid quantum-classical optimization to solve optimization tasks at full device scale without deep quantum expertise. |

scripts/js/lib/links/ignores.ts

@@ -192,6 +192,13 @@ const ALWAYS_IGNORED_URLS__EXPECTED = [
   "https://finance.yahoo.com/quote/META",
   "https://finance.yahoo.com/quote/TMBMKDE-10Y",
   "https://finance.yahoo.com/quote/XS2239553048",
+  "https://ibm.box.com/s/7d36ty2r5ecmnnu1bvk4kxx5grbz2mwz",
+  "https://ibm.box.com/s/cbvfdhv3i0vnwv4sxkdpp3pcvip27atd",
+  "https://ibm.box.com/s/eylihre0nf2lskzteyabd7y7v7cu5bb3",
+  "https://ibm.box.com/s/h7ocmsqgwjuj493b46m1nrkqkazh4g4j",
+  "https://ibm.box.com/s/ly9cs0lxy6nxqrirt5rt1xisnmvydj9z",
+  "https://ibm.box.com/s/oe621o30gxvpo8v0tivp31075w9wc6t8",
+  "https://ibm.box.com/s/t9tj2t8ey8hh1iep3jr88yzlqbqdvq78",
   "https://ibm.ent.box.com/s/bipgoms7gr6b6vhkoc1uw6oi4wsanfoq",
   "https://ibm.ent.box.com/s/blnffu0pd7yzxarq3zc3w0jv90365ny2",
   "https://ibm.ent.box.com/s/fh3xele1e7k0nrgd1imivvq52hy3wz9c",